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/

braces Suck! :: essays research papers

"Braces Suck!" One out of three children or teenagers will have to live, at one point, as a prisoner of their own dentist. Teenagers are faced with zit and acne wars during the stages of puberty and braces add additional torture to this already hellish time to both parent and child. A life with braces is far more embarrassing, painful, and expensive than living with buck-teeth, gaps, or overlapping teeth. Mental scars remain long after cuts and bloody sores in the mouth have healed. These metal-like plates come with a long list of insults and nicknames. All through school one can expect to be called brace-face, Jaws and metal mouth just to name a few. The 'orthodontically' challenged are always the center of electricity and lip-locking jokes. The dentist's office is also a source of embarrassment. Most offices are filled with other patients and operating rooms are easily accessible making it easy for others to watch the pain and embarrassment the patient has to goes through. If one should forget to brush their teeth before their visit, they will regrettably become immortal as the doctor announces the left-over remains of a Turkey and Cheese sandwich stuck between the molars. Braces become a constant source of embarrassment. Braces are three to four years of physical torture beginning with the very first office visit. The applying of the brackets itself is long, tiresome, and uncomfortable. First, cold, flavored clay is shoved into the inside of the mouth, forming a mold as it dries. Jagged metal squares (brackets) are glued to the tooth, forcing hot, burning, glue to drip down the gums. Braces also cause everyday aches and pains in the mouth. Metal wires, guiding teeth to a new shape, stab the inside of the mouth causing cuts and sores while tearing the linings of the mouth each time a person's mouth opens. Rubber bands that are strung across each of the brackets pull and stretch teeth until gums are painful and sore. Being born with imperfect teeth can be painful†¦trust me! Braces hurt parents' wallets well after the metal and glue is scraped and chiseled off. Payments while braces are being worn are unbelievable. The average cost of braces today is around 10 thousand dollars.

Fast-Food Industry: Friend or Foe? Essay

The 2004 American documentary known as Super-Size Me left a remarkable impact on America’s fast-food industries, as well as fellow fast-food consumers. Not to mention, six weeks after Super-Size Me was released, McDonalds took the â€Å"Super-Size† option off their menu as well as their stress on healthier menu choices; such as salads, fruit, and the new adult happy meal. The director, writer, and producer of Super-Size Me is also starring in the film himself, he is Morgan Spurlock. This documentary is anything but flashy or cinematically amazing; it purely presents the real story of Morgan’s journey to a healthier America. Americans know how addicting fast-food really is, but what they don’t know is what fast-food does to their bodies over time. Super-Size Me did influence McDonalds and our society as a whole, however have we still been a healthier America since then. The main point for Spurlock’s experiment was simply, the growing spread of obesit y in our society. There was even a lawsuit that was brought against McDonald’s by two overweight girls, who later became obese because of eating McDonald’s food. But as you would guess, the lawsuit failed. As Super-Size Me starts, Morgan Spurlock is at an above average shape condition in respect of his personal trainer. He is then seen by three doctors: a cardiologist, a gastroenterologist, a general practitioner, as well as a nutritionist and a personal trainer. Morgan Spurlock is documented for thirty days from February 1st to March 2, 2003, in which he eats only McDonald’s food. Yes that means for breakfast, lunch, and dinner; not to mention every time he is asked to â€Å"super-size† his meal Spurlock must super-size it. Eating McDonald’s all day made his calorie intake for each day approximately 5,000 calories, which is equal to nine Big Macs! This movie is pretty straight-forward going along with the title, however along the way Spurlock visits elementary schools to see how healthy their food options are. He also does some speeches at schools for the kids, warning them the dangers of unhealthy food choices as well as getting active every day. As well as inter viewing random people he meets on the street and at McDonald’s restaurants. Spurlock asks them about their eating habits and why they chose to eat at fast-food instead of cooking at home. Majority of the people interviewed chose fast-food because it was easy, fast, and of course just darn delicious. Also many of them didn’t seem too concerned for their  health, or how much McDonalds they ate in a week. Some even refused to answer Spurlock’s questions they had negative actions towards his experiment. This is not surprising, many people especially children have no worries about what fast-food does to their body; they just know it tastes good and is a quick fix. As you can tell, this movie is not all about a crazy guy eating McDonald’s for weeks; it also has great nutritional facts and a look at how unhealthy America is compared to other countries. Towards the end of the movie, Spurlock finds out the results of his thirty-day challenge. He gained twenty-four and a half pounds, a thirteen percent body mass increase, a cholesterol level of 230, experienced mood swings, sexual dysfunction, and fat accumulation in his liver. Not only that, it took him fourteen months to lose the weight he gained during this Super-Size Me experiment. The documentary closes with an interesting question, asking â€Å"Who do you want to see go first, you or them?† Super-Size Me can be a love-hate relationship for most people who get the chance to watch it. If you love McDonald’s and don’t have much care for eating right this movie wouldn’t be for you; on the other hand, if you are displeased with the fast-food industry in America and interested in seeing how it affects people, this would be a great movie for you. For me, I really enjoyed this movie; it opened my eyes about how overweight and unhealthy we Americans are. You would not believe what fast-food does to your body over time, and how it changes your body steadily without you knowing a thing! I still love and consume fast-food to this day, but I definitely try my very best to not take part as much as I did before. Granted, not every person that watches Super-Size Me will get the same inspirational, mind-blowing feeling to change their eating habits . However, I strongly feel in my gut that this documentary changed a lot of people, whether they were a part of the movie or just a viewer. I just really hope that we Americans have stayed true to the facts of Super-Size Me and have not forgotten the effects of constant fast-food eating.

The Importance Of Cultural Diversity For Company Success

(understand the importance of being honest, ethical and fair) and diversity (understand the importance of cultural diversity for company success). (Adidas Careers, 2015) Corporate Governance and Risk Management Adidas, being a multi-national enterprise contributes decently towards the global economy and society. They are aware of the laws, rules and regulations (formal institution) in addition to putting efforts to become a globally socially responsible firm. A group named Social and Environmental Affairs (SEA) is part of their sustainability efforts. Adidas has built a risk management framework and the SEA group which enhances their environment to conduct business. The group is a team consisting of persons from various functions like, engineers, environmental reviewers, human resource managers, and few former members of non-governmental groups. The team is organized into three groups spanning Asia, America and Europe and Middle East and Africa. (Adidas3, 2015). The team members are spread out across the world, which is a much needed mix from across the world for diversity. The group discusses and take resolution for issues or initiates from across various parts of the world. They are famil iar with their culture and are trained how to work in a diverse culture work environment. The group provides upper management with up to date information on all the activities and social and environmental related issues from across all their business functions worldwide. The majorShow MoreRelatedDiversity In Todays Organizations Essay example1136 Words   |  5 Pagesmaximize the benefits of the differences in employees, organizations are relying on managers to get the people who get the job done. People have always been the central to organizations, but there strategic importance is growing in todays knowledge-based business world. 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