Building Effective Teams & Managers and leaders

Whether we like it or not, there are always poor performers in any type of team or organization. These are employees or team members who do not meet the standards set by the organization. Commonly, poor performing employees are those who belong to Quartile Four when employees are force-ranked. Most managers and leaders agree that it’s not easy dealing with poor performers. As Levinson (2003) said, â€Å"it’s a wrenching task, but you have to face up to the need to confront poor performers, and either fix their shortcomings or fire them.  Ã¢â‚¬ Poor performers, no matter how small in number they may be, still have a big impact in the performance of the whole team or organization. As the HR Manager of the company, I would advise each member of the team to help one another in their tasks and job responsibilities. If the team members notice that there are poor performers among them, they should take immediate action by working with these people and talking to them. Havi ng a good peer-to-peer conversation may reveal the issues that the employee is facing, thus, affecting his/her performance at work. This can help getting to the root cause of the problem and help the member solve it.Team members who are not performing well tend to share their problems and open up more easily with their peers than with their supervisor or manager. Poor performers can also be identified through feedback from peers and, if applicable, customers. The customers are the people who can see the outputs of the employee or member’s work. If it is unsatisfactory to the customer, then the employee must not be performing well. Peers are good sources of feedback when it comes to how the employee or team member really works when the boss is not around. The employee’s co-workers are the people s/he gets to work with day in and day out.They are the ones who can immediately see where the employee is good at and where he is not. Thus, these people can better provide the data and the tools to determine the poor performers in the team or organization. Though co-workers may contribute to the improvement of a poor performer, still, the best person who could talk to the person and give relevant advices is the immediate superior. The member’s immediate superior would know the areas where s/he needs more improvement. Thus, the superior can give better advice pertaining to these areas and how to better improve on them.The immediate superior can also give suggestions on some actions the member needs to do in order to improve on the areas identified. Coaching and mentoring are the key techniques in handling poor performers. Constant monitoring of the employee’s performance and regular coaching would do a lot of help in the improvement of the employee. People think that coaching is a negative thing when it is actually the opposite. Coaching provides a venue for both the employee and supervisor to talk about each other’s performance (Yes, employees get to speak to! ).However, if several coaching have already been done and all other mentoring techniques and help are also tried but the performance of the employee remains the same, it will be best for both parties for the employee to just say goodbye. The job may just not really be for him/her. References Levinson, M. (2003, November 1). How to Find, Fix or Fire Your Poor Performers. CIO Magazine. Retrieved August 1, 2006 from http://www. cio. com/archive/110103/poor. html Time to Stop Tolerating Poor Performers. (2006, February 26). The Sunday Times. Retrieved August 1, 2006 from http://www. timesonline. co. uk/article/0,,8543-2057887_1,00. html

The Journey of Sojouner Truth essays

The Journey of Sojouner Truth essays Her name is Isabella Van Wagner born into Slavery in 1797. She was one of twelve children parented by Elizabeth and James Wagner. Her brother and sisters were scattered on different plantations throughout the world. Therefore, she never knew any of them. She was from Hurley, Ulster County, New York. Charles Hardenbergh owned both of her parents. John J. Dumont owned her. Therefore, she never had any physical contact with her parents. Isabella had an arranged marriage to a man named Thomas. The two of them had five children throughout their marriage. All of her children were sold as slaves. Imagine how she felt having to watch her children sold to hard life of brutality. Isabella could not do anything about it. In order to survive as Isabella did through slavery, strength and courage were the main factors. It has been said by many researchers that Isabella ran away from Dumont in 1827. However, it has also been said that she was released following the New York Anti Slavery Law of 1827. Therefore, the truth is very unclear to me. I am not sure how Isabella was freed. After she was freed, she lived with a Quaker family. She stayed there for quite some time using their surname. During the time that she stayed there, they helped her retrieve one of her daughters by the name of Sophia. From 1829 to 1843, Isabella supported herself by domestic work, at the same time she was searching for lifes deeper thoughts and meanings. She became a religious mystic and took on the name Sojourner Truth. Trying to save the world by preaching against sin, including the sin of slavery. She was an outspoken advocate of womens rights. Sojourner spoke with a deep voice, and powerful words leaving a long lasting impression wherever she spoke. Sojourner Truth was a strong determined black woman and her words had an effect on everyone she spoke too. In 1851, Sojourner Truth became famous for her speech at the Womens Rights Convent...

New challenges in taking GCSEs

New challenges in taking GCSEs UK educational system I’d like to share with you some latest news about UK educational system and specifically challenges in taking GCSEs UK examination system for secondary school undergoes serious changes. According to the recently adopted rules, children will have to spend three years, instead of two, to get ready for math and English - two main GCSE subjects. The main goal of these reforms is to make exams more complicated and thus more productive. They are mainly focused on branches of mathematics, such as algebra and geometry. According to these changes, school children are expected to make in-depth investigation of the subjects and learn the material more profoundly. Undoubtedly, the time spent for learning the subject will be increased as well. Teenagers are likely to have more math classes during the week alongside with the extended curriculum. The new GCSE project is to be launched in 2018. Despite such cushion of time, designers of the examination system recommend that schoolchildren get down to work as soon as possible, since the renovated program includes all the additional math exams. Teenagers are not the only ones, who will be crammed with the material. A heavy burden of mixed responsibilities will be laid on teachers’ shoulders. The range of their duties is going to be enriched with the following tasks: to keep up with the updates in fundamentals of the subjects, to monitor pupils’ progress, to work out new syllabus. In addition, the changes will concern not only the quality of the exams but also their quantity.   The number of subjects is reported to be reduced from 12 to 8. Therefore, academic progress of the schools will be ranked in eight key subjects. They are as follows: English and Baccalaureate subjects of English, mathematics, science, language and humanities subjects. The grade system is also said to be renewed. The well-known grade letters A, B, C, D, E are expected to be substituted with numbers. Thus, there are going to be two test papers at pupils’ disposal. The first one, in higher mathematics, is graded from 4 to 9, for the second, less complicated test one can get from 1 to 5 points. Experts assure that new GCSE project is worth all the efforts and time. It is expected that those pupils, who usually leg behind the rest of the class will have an opportunity to boost their success in studies, as they will have more time to get ready for GCSEs.

Summary of Personal Reflections and Learning In Relation To My SWOT Essay

Summary of Personal Reflections and Learning In Relation To My SWOT and PDP - Essay Example Further, I become familiar with the university setting and learned that the only difference from other lower learning institutions was the fact that one had to be self- driven. In relation to this, I learned a significant number of behaviors characteristic of me. In other words, I have come to learn and appreciated some attributes that define my personality. It was with the help of a questionnaire that colleagues at the university were able to guide me towards self-discovery. However, there are some attributes inherent in my personality that I would like to work on. I call them personal weaknesses. This paper summarizes my reflection and learning in relation to my strengths, weakness, opportunities, and threats. Further, I will present my personal development plan. This exercise will help me to identify my weakness, and improve on them. Consequently, I will be able to improve my chances of being employed in the future. After two weeks of attending classes, I discovered some strengths about myself. In my case, I could easily start a conversation, thus most people approached me for help regarding some personal problems. During lectures, students will request me to ask the lecturer regarding class materials. At some point, I was voted as the class representative owing to my outgoing nature. Hence, I came to learn that I was confident, but others called it daring. Overall, what they meant is that I had the courage to express myself whenever I felt like doing it. I learned that I had taken this attribute for granted while growing up. I hardly knew that it was not in the nature of everyone to take charge and face issues without apprehension. Based on Eysenck’s theory of personality, some people’s personality is characterized as extroverted- stable. These types of people are sociable, outgoing, responsive, and could be able to lead others (Nevid, 2010).

Technical Writing Essay Example | Topics and Well Written Essays - 1000 words - 3

Technical Writing - Essay Example how to sell, customer service, merchandise knowledge. By the end of the third month, the employee flows without assistance. Please do not hesitate to contact me in regards to any questions or concerns about this report. Sincerely, YOUR NAME 1. Course- Mythology (English Department) Lesson- Odes Assignment – Greek and Roman View on Life and Death in Horace’s Odes. Five page paper. Works Cited Page. Minimum sources 3. Deadline: two weeks. Step One: Obtain correct materials. In this case, the Jame’s Michie translation of Horace’s Odes, 2002. Step Two: Read and highlight portions of Horace’s Odes that pertain directly to life and death. Note line and page numbers. Step Three: Seek other credible sources that better explain or discuss the idea of how Romans viewed life and death that refer to Horace’s poems and cite them with evidence in the main source to back your argument. Step Four: Write the paper with the correct amount of pages and sources. Reread for grammar and spelling then again for clarity and flow. Step Five: Make sure to turn the paper in by the deadline to avoid losing unnecessary points. 2. The particular lesson above will be graded as follows: A. Compliance of instructions (15 points) B. Content (25 points) C. Grammar and spelling (20 points) D. Ease of explanation, flow, proper citation, evidence, and relevancy (30 points) E. Works Cited or Reference Page (10 points) *5 points will be deducted for every day past due date on late papers. After 7 days, there will be no late papers accepted. a-c. The technical writer’s responsibilities include developing documentation via editing, proofreading, writing, research, and professionalism. Also, delivering proposals, preparing grants and/or writing for operational systems. Understanding a reader’s context affects the preparation of a document in numerous ways, but the most important ones being that the wrong research could be conducted, or content edit ed if that communication is null n void. Project Plan sheets have six parts to it: Audience, Purpose, Subject, Author, Project Design and Specifications, and Due Date. The individual functions act as a whole to ensure an appropriate, detailed plan that assists in avoiding future errors. 3. a. Sexist language is language that unnecessarily draws attention to gender in a negative way, i.e. to stereotype or demean. b. Bar Charts are visual tools to illustrate a point. They can also be graphs and have rectangular-shaped bars that are shaded or not shaded according to the values in which are utilized, or being presented. It does not matter if they are showed vertically or horizontally as long as it is consistent with the data. c. Spatial method of paragraph development is effective for optimizing description and it moves directionally. d. The classification method of paragraph development is not as left to right so to speak because it goes by grouping people or objects by their shared gr oups. Reference Page Picket, A. N., Laster, A. A., & Staples, K. E. (2001). Technical English: Writing, Reading, and Speaking (8th ed., pp. 45-46). New York, NY: Addison Wesley

Jury Selection Essay Example | Topics and Well Written Essays - 1000 words

Jury Selection - Essay Example Written by Neil Kressel, a social psychologist at New Jersey's William Paterson University, and his wife Dorit, a practicing attorney, this book provides an even-handed accounting of the methods and ethical issues of the phenomenon called jury consultancy and its possible implications for American justice. It provides a discussion regarding the use of jury consultants in sensitive matters such as race and answers the questions: What do jury consultants do Are their elaborate efforts to assist lawyers in the jury selection process by identifying attitudes, values, and would-be demographic predictors merely benign efforts to screen for biases that could jeopardize fair trials, as practitioners like to claim Scientific Jury Selection is a well-written volume that reviews the research and issues surrounding scientific jury selection. The authors examine the many factors and methods involved in this process and provide a balanced and comprehensive review of the literature as well as raise important scientific and ethical questions. Chapters review such factors as methods of acquiring information and applying those methods to the actual process of jury selection. The volume raises substantial issues about the accuracy and efficacy of the selection process, as well as its ethical and legal implications. In addition, it provides the basis for the psychological methods used. 4. A. Austin (1984). Complex Litigation Confronts the Jury System, 103-104. Greenwod Press, US. Austin provides a case study in which one could gain valuable insight into the workings of jury consultancy and provides an analysis and possible implications of the methods used thru the case study presented. 5. Leci, L., Snowden, J. and Morris, D (2004). "Using Social Science Research to Inform and Evaluate the Contributions of Trial Consultants in the Voir Dire." Journal of Forensic Psychology Practice 4.2 (2004) 67-78 The authors argue that the jury selection methods commonly employed by trial consultants and lawyers in the voir dire process are fraught with problems because they do not employ standardized assessments. This commentary provides and advocates the advantages of employing standardized, reliable, and validated measures of pretrial juror bias to more effectively conduct the voir dire, and we delineate some of the methods by which this can be accomplished. 6. Lieberman, Joel D., and Bruce D. Sales (2007). "Overall Effectiveness of Scientific Jury Selection" in PsycINFO. Washington DC, US: American Psychological Association, 2007. Lieberman and Sales provides a discussion on matters of jury consultancy such as the Purpose and effectiveness of the Voir Dire, influence of demographic factors, influence of Personality and Attitudes, in-court questioning of prospective jurors and ethical and professional issues in Scientific Jury Selection. 7. Van Wallendael, Lori, and Brian Cutler (2004).

Emotion Recognition From Text-a Survey

Emotion Recognition From Text-a Survey Ms. Pallavi D. Phalke , Dr. Emmanuel M. ABSTRACT Emotion is a very important facet of human behaviour which affect on the way people interact in the society. In recent year many methods on human emotions recognition have been published such as recognizing emotion from facial expression and gestures, speech and by written text. This paper focuses on classification of emotion expressed by the online text, based on predefined list of emotion. The collection of dataset is the basic step, which is collected from the various sources like daily used sentences, user status from various social networking websites such as  facebook and twitter. Using this data set we target only on the keywords that show human emotions. The targeted keywords are extracted from the dataset and translated into the format which can be processed by the classifier to finally generate the Predicting model which is further compared by the test dataset to give the emotions in the input sentences or documents. Keywords— Affective Computing, Classification, Document Categorization, Emotion Detections. INTRODUCTION Recently much research is going on in emotion recognition domain. Recognition of emotions is very useful to human-machine communication. Many kinds of the communication system can react properly for the humans emotional actions by applying emotion recognition techniques on them. These systems include dialogue system, automatic answering system and robot. The recognition of emotion has been implemented in many kinds of media, such as image, speech, facial expressions, signal, textual data, and so on. Text is the most popular and main tool for the human to convey messages, communicate thoughts and express inclination. Textual data make it possible for people to exchange opinions, ideas, and emotions using text only. Therefore the research for recognizing from the textual data is valuable. Keyword-based approach to the proposed system since the keyword-based approach shows high recognizing accuracy for emotional keywords. Interaction between humans and computers has been increased with increase in development of information technology. Recognizing emotion in text from document or sentences is the first step in realizing this new advanced communication which includes communication of information such as how the writer/speaker feels about the fact or how they want the reader/listener to feel. Analyzing text, detecting emotions is useful for many purposes, which includes identifying what emotion a newspaper headline is trying to evoke, identifying users emotion from their statuses of different social networking sites, devising dialogue systems that respond appropriately to different emotional states of the user and identifying blogs that express specific emotions towards the topic of interest. List of emotions and words that are indicative of each emotion is likely to be useful in identifying emotions in text because, many times different emotions are expressed by different words. For example cry and glo omy are indicative of sadness, boiling and shout are indicative of anger, yummy and delightful indicate the emotion of joy. To capture emotion from text document we require the classification which aims at presume the emotion conveyed by the documents based on predefined lists of emotion, such as Joy, Anger, Fear, Disgust, Sad and Surprise. This emotion recognition approach is mainly focused on two main tasks. 1) The test data that is text document collected from any news articles, user statuses from different social networking sites etc. required for understanding the emotions evoked by words. This is because a different word arouses different emotions comprehended from our day to day experiences. For this purpose, need is to enhanced dictionary with emotion word from ISEAR, WorldNet Affect to improve in result. 2) Need for text normalization to handle negation, since the scope of words is larger in this scenario, the usage of words and their diverted form is large too. So these problems need to be solved properly. The next part of this paper is organised as follows: Section II discusses a survey of emotion detection from text, Section III describes different algorithms on different datasets for emotion recognition, Section IV briefly compares proposed work followed by experimental study with result in section V and Section V concludes the paper. THE SURVEY OF EMOTION DETECTION FROM TEXTS Definitions about emotion, its categories, and their influences have been an important research issue long before computers emerged, so that the emotional state of a person may be inferred under different situations. In its most common formulation, the emotion detection from text problem is reduced to finding the relations between specific input texts and the actual emotions that drives the author to type/write in such styles. Intuitively, finding the relations usually relies on specific surface texts that are included in the input texts, and other deeper inferences that will be formally discussed below. Once the relations can be determined, they can be generalized to predict others’ emotions from their articles, or even single sentences. At the first glance, it does not seem to involve so many difficulties. In real life, different people tend to use similar phrases (i.e. â€Å"Oh yes!†) to express similar feelings (i.e. joy) under similar circumstances (i.e. achieving a goal); even they native languages are different, the mapping of such phrases from each language may be obvious. More formally, the emotion detection from text problem can be formulated as follows: Let E be the set of all emotions, A be the set of all authors, and let T be the set of all possible representations of emotion-expressing texts. Let r be a function to reflect emotion e of author a from text t, i.e., r: A Ãâ€" T → E and the function r would be the answer to our problem. The central problem of emotion detection systems lies in that, though the definitions of E and T may be straightforward from the macroscopic view, the definitions of individual element, even subsets in both sets of E and T would be rather confusing. On one hand, for the set T, new elements may add in as the languages are constantly evolving. On the other hand, currently there are no standard classifications of â€Å"all human emotions† due to the complex nature of human minds, and any emotion classifications can only be seen as â€Å"labels† annotated afterwards for different purposes. As a result, before seeking the relation function r, all related research firstly define the classification system of emotion classifications, defining the number of emotions. Secondly, after finding the relation function r or equivalent mechanisms, they still need to be revised over time to adopt changes in the set T. In the following subsections, we will present a classification of emotion detection methods proposed in the literature, based on how detection are made. Although they can all be classified into content-based approaches from the point of view of information retrieval, their problem formulation differs from each other: 1. Keyword-based detection: Emotions are detected based on the related set(s) of keywords found in the input text; 2. Learning-based detection: Emotions are detected based on previous training result with respect to specific statistic learning methods; 3. Hybrid detection: Emotions are detected based on the combination of detected keyword, learned patterns, and other supplementary information; Besides these emotion detection methods that infer emotions at sentence level, there has been work done also on detection from online blogs or articles [1][2]. For example, though each sentence in a blog article may indicate different emotions, the article as a whole may tend to indicate specific ones, as the overall syntactic and semantic data could strengthen particular emotion(s). However, this paper focuses on detection methods with respect to single sentences, because this is the foundation of full text detection. A. KEYWORD-BASED METHODS Keyword-based methods are the most intuitive ways to detect textual emotions. To approximate the set T, since all the names of emotions (emotion labels) are also meaningful texts, these names themselves may serve as elements in both sets of E and T. Similarly, those words with the same meanings of the emotion labels can also indicate the same emotions. The keywords of emotion labels constitute the subset EL in set T, where EL also classifies all the elements in E. The set EL is constructed and utilized based on the assumption of keyword independence, and basically ignores the possibilities of using different types of keywords simultaneously to express complicated emotions. Keyword-based emotion detection serves as the starting point of textual emotion recognition. Once the set EL of emotion labels (and related words) is constructed, it can be used exhaustively to examine if a sentence contains any emotions. However, while detecting emotions based on related keywords is very straightforward and easy to use, the key to increase accuracy falls to two of the pre-processing methods, which are sentence parsing to extract keywords, and the construction of emotional keyword dictionary. Parsers utilized in emotion detection are almost ready-made software packages, whereas their corresponding theories may differ from dependency grammar to theta role assignments. On the other hand, constructing emotional keyword dictionary would be naval to other fields [3]. As this dictionary collects not only the keywords, but also the relations among them, this dictionary usually exists in the form of thesaurus, or even ontology, to contain relations more than similar and opposite ones. Semi-automatic construction of EL based on WorldNet-like dictionaries is proposed in [4] and [5]. As was observed in [6], keyword-based emotion detection methods have three limitations described below. 1) AMBIGUITY IN KEYWORD Though using emotion keywords is a straightforward way to detect associated emotions, the meanings of keywords could be multiple and vague. Except those words standing for emotion labels themselves, most words could change their meanings according to different usages and contexts. It is not feasible to include all possible combinations into the set EL. Moreover, even the minimum set of emotion labels (without all their synonyms) could have different emotions in some extreme cases such as ironic or cynical sentences. 2) INCAPABILITY OF RECOGNIZING SENTENCES WITHOUT KEYWORDS As Keyword-based approach is totally based on the set of emotion keywords, sentences without any keywords would imply like they don’t contain any emotions at all, which is obviously wrong. 3) LACK OF LINGUISTIC DATA Syntax structures and semantics also affect on expressed emotions. For example, â€Å"He laughed at me â€Å"and â€Å"I laughed at him† would suggest different emotions from the first person’s point of view. Therefore, ignoring linguistic information also create a problem to keyword-based methods. B. LEARNING-BASED METHODS Researchers using learning-based methods attempt to formulate the problem differently. The original problem that determining emotions from input texts has become how to classify the input texts into different emotions. Unlike keyword-based detection methods, learning-based methods try to detect emotions based on a previously trained classifier, which apply various theories of machine learning such as support vector machines [7] and conditional random fields [8], to determine which emotion category should the input text belongs. However, comparing the satisfactory results in multimodal emotion detection [9], the results of detection from texts drop considerably. The reasons are addressed below: 1) DIFFICULTIES IN DETERMINING EMOTION INDICATORS The first problem is, though learning-based methods can automatically determine the probabilities between features and emotions, learning-based methods still need keywords, but just in the form of features. The most intuitive features may be emoticons, which can be seen as author’s emotion annotations in the texts. The cascading problems would be the same as those in keyword-based methods. 2) OVER-SIMPLIFIED EMOTION CATEGORIES Nevertheless, lacking of efficient features other than emotion keywords, most learning-based methods can only classify sentences into two categories, which are positive and negative. Although the number of emotion labels depends on the emotion model applied, we would expect to refine more categories in practical systems. C. HYBRID METHODS Since keyword-based methods with thesaurus and naà ¯ve learning-based methods could not acquire satisfactory results, some systems use a hybrid approach by combining both or adding different components, which help to improve accuracy and refine the categories. The most significant hybrid system so far is the work of Wu, Chuang and Lin [6], which utilizes a rule-based approach to extract semantics related to specific emotions, and Chinese lexicon ontology to extract attributes. These semantics and attributes are then associated with emotions in the form of emotion association rules. As a result, these emotion association rules, replacing original emotion keywords, serve as the training features of their learning module based on separable mixture models. Their method outperforms previous approaches, but categories of emotions are still limited. D. SUMMARY AND CONCLUSIONS As described in this section, much research has been done over the past several years, utilizing linguistics, machine learning, information retrieval, and other theories to detect emotions. Their experiments show that, computers can distinguish emotions from texts like humans, although in a coarse way. However, all methods have certain limitations, as described in the previous subsections, and they lack context analysis to refine emotion categories with existing emotion models, where much work has been done to put them computationalized in the domain of believable agents. On the other hand, applications of affective computing would expect more refined results of emotion detection to further interact with users. Therefore, developing a more advanced architecture based on integrating current approaches and psychological theories would be in a pressing need. III. ALGORITHMS USED IN EMOTION RECOGNITION A brief summary of the various works for emotion recognition discussed in this paper are presented in Table1. Table 1: Results and feature-set comparison of algorithms IV.EMOTION RECOGNITION IN SOCIAL COMMUNICATION The block diagram of the emotion recognition system studied in this paper is depicted in Figure 1.It contains three main modules: Affective communication unit, Data Aggregator, Emotion Recognition Engine and recognized emotion class as an output. Figure 1 : Block diagram of emotion recognition system for Affective communication AFFECTIVE COMMUNICATION UNIT Affective Communication Unit is nothing but the users account in any social networking site (tweeter or facebook). This system take input from these two social networking sites. DATA AGGREGATOR Data Aggregator collects user tweets and status from tweeter and facebook. These tweets/status serve as an input to Emotion Recognition Engine. EMOTION RECOGNITION ENGINE Emotion Recognition Engine including Bayesian Network classifier categorizes incoming data into 3 types of emotions: happiness, sadness, and neutral, because this system mainly focuses on finding stress level of user. It is broken up into 2 major phase: Training Phase and Testing Phase. Training phase consist of five important parts: The Training Dataset, Keyword Extraction, Keyword conversion, Training Model and Predicting Model. Before it generate the predicting model or file, training phase get the training dataset from which it extracted the keyword from the emotion training date, and convert the keyword using keyword conversion into the format that can be processed by the classifier in the Training Model. Testing phase which is also called predicting phase consist of Testing dataset, Keyword extraction, Keyword conversion and predict model. The testing phase extract the Keyword from the given sentence, which was the input from the keyboard and then translate the keyword (word of natural language) using the Keyword conversion into the format that can be processed and then we compare it with a predicting file in predict module and finally gives the output as appropriate emotion expressed by the text. VI.CONCLUSION The proposed system is able to recognize the happy and sad state of a person from his tweets posted on tweeter from his mobile. The experimental results Shows that the we get better accuracy using Naive Bayes classifier than that of Support Vector Machine. VII. REFERENCES [1] 2. Tim M.H. Li, Michael Chau, Paul W.C. Wong, and Paul S.F. YipA Hybrid System for Online Detection of Emotional Distress PAISI 2012, LNCS 7299 Springer-Verlag Berlin Heidelberg 2012M, 73–80. [2] Abbasi, A., Chen, H., Thoms, S., Fu, T.: â€Å"Affect Analysis of Web Forums and Blogs Using Correlation Ensembles.† IEEE Transactions on Knowledge and Data Engineering (2008) ,1168–1180. [3] T. Wilson, J. Wiebe, and R. Hwa, â€Å"Just how mad are you? Finding strong and weak opinion clauses,† Proc. 21st Conference of the American Association for Artificial Intelligence Jul. 2007, 761-769. [4] D. B. Bracewell, â€Å"Semi-Automatic Creation of an Emotion Dictionary Using WordNet and its Evaluation,† Proc. IEEE conference on Cybernetics and Intelligent Systems, IEEE Press, Sep. 2008, 21-24. [5] J. Yang, D. B. Bracewell, F. Ren, and S. Kuroiwa, â€Å"The Creation of a Chinese Emotion Ontology Based on HowNet†, Engineering Letters, Feb. 2008,166-171. [6] C.-H. Wu, Z.-J. Chuang, and Y.-C. Lin, â€Å"Emotion Recognition from Text Using Semantic Labels and Separable Mixture Models,† ACM Transactions on Asian Language Information Processing Jun. 2006, 165-183. [7] Z. Teng, F. Ren, and S. Kuroiwa, â€Å"Recognition of Emotion with SVMs,† in Lecture Notes of Artificial Intelligence Eds.Springer, Berlin Heidelberg, 2006,701-710 . [8] C. Yang, K. H.-Y. Lin, and H.-H. Chen, â€Å"Emotion classification using web blog corpora,† Proc. IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, Nov. 2007, 275-278. [9] C. M. Lee, S. S. Narayanan, and R. Pieraccini, Combining Acoustic and Language Information for Emotion Recognition, Proc. 7th International Conference on Spoken Language Processing (ICSLP 02), 2002, 873-876. [10]http://www.affectivesciences.org/reserachmaterial [11] http://www.weka.net.nz/