Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01 in F-measure.
While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting - in a totally automated way - a high-coverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a na " ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building. Sentiment Similarity of word pairs reflects the distance between the words regarding their underlying sentiments. This paper aims to infer the sentiment similarity between word pairs with respect to their senses. To achieve this aim, we propose a probabilistic emotionbased approach that is built on a hidden emotional model. The model aims to predict a vector of basic human emotions for each sense of the words. The resultant emotional vectors are then employed to infer the sentiment similarity of word pairs. We apply the proposed approach to address two main NLP tasks, namely, Indirect yes no Question Answer Pairs inference and Sentiment Orientation prediction. Extensive experiments demonstrate the effectiveness of the proposed approach.  Emotion classification can be generally done from both the writer’s and reader’s perspectives. In this study, we find that two foundational tasks in emotion classification, i.e., reader’s emotion classification on the news and writer’s emotion classification on the comments, are strongly related to each other in terms of coarse-grained emotion categories, i.e., negative and positive. On the basis, we propose a respective way to jointly model these two tasks. In particular, a cotraining algorithm is proposed to improve semi-supervised learning of the two tasks. Experimental evaluation shows the effectiveness of our joint modeling approach. Microblog has become a major platform for information about real-world events. Automatically discovering realworld events from microblog has attracted the attention of many researchers. However, most of existing work ignore the importance of emotion information for event detection. We argue that people’s emotional reactions immediately reflect the occurringofreal-worldeventsand shouldbeimportant for event detection. In this study, we focus on the problem of communityrelated event detection by community emotions. To address the problem, we propose a novel framework which include the following three key components: microblog emotion classification, community emotion aggregation and community emotion burst detection. We evaluate our approach on real microblog data sets. Experimental results demonstrate the effectiveness of the proposed framework.  In the past years, there has been a growing interest in developing computational methods for affect detection from text. Although much research has been done in the field, this task still remains far from being solved, as the presence of affect is only in a very small number of cases marked by the presence of emotion-related words. In the rest of the cases, no such lexical clues of emotion are present in text and special commonsense knowledge is necessary in order to interpret the meaning of the situation described and understand its affective connotations. In the light of the challenges posed by the detection of emotions from contexts in which no lexical clue is present, we proposed and implemented a knowledge base – EmotiNet – that stores situations in which specific emotions are felt, represented as “action chains”. Following the initial evaluations, in this chapter, we describe and evaluate two different methods to extend the knowledge contained in EmotiNet: using lexical and ontological knowledge. Results show that such types of knowledge sources are complementary and can help to improve both the precision, as well as the recall of implicit emotion detection systems based on commonsense knowledge.   While there have been many attempts to estimate the emotion of an addresser from her his utterance, few studies have explored how her his utterance affects the emotion of the addressee. This has motivated us to investigate two novel tasks: predicting the emotion of the addressee and generating a response that elicits a specific emotion in the addressee’s mind. We target Japanese Twitter posts as a source of dialogue data and automatically build training data for learning the predictors and generators. The feasibility of our approaches is assessed by using 1099 utterance-response pairs that are built by five human workers. Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion. There is plenty of evidence that emotion analysis has many valuable applications. In this study a blog emotion corpus is constructed for Chinese emotional expression analysis. This corpus contains manual annotation of eight emotional categories (expect, joy, love, surprise, anxiety, sorrow, angry and hate), emotion intensity, emotion holder target, emotional word phrase, degree word, negative word, conjunction, rhetoric, punctuation and other linguistic expressions that indicate emotion. Annotation agreement analyses for emotion classes and emotional words and phrases are described. Then, using this corpus, we explore emotion expressions in Chinese and present the analyses on them. We present a weakly supervised approach for learning hashtags, hashtag patterns, and phrases associated with five emotions: AFFECTION, ANGER RAGE, FEAR ANXIETY, JOY, and SADNESS DISAPPOINTMENT. Starting with seed hashtags to label an initial set of tweets, we train emotion classifiers and use them to learn new emotion hashtags and hashtag patterns. This process then repeats in a bootstrapping framework. Emotion phrases are also extracted from the learned hashtags and used to create phrase-based emotion classifiers. We show that the learned set of emotion indicators yields a substantial improvement in F-scores, ranging from + 5 to + 18 over baseline classifiers.