Detecting emotions from text is an extension of simple sentiment polarity detection. Instead of considering only positive or negative sentiments, emotions are conveyed using more tangible manner; thus, they can be expressed as many shades of gray. This paper manifests the results of our experimentation for fine-grained emotion analysis on Bangla text. We gathered and annotated a text corpus consisting of user comments from several Facebook groups regarding socio-economic and political issues, and we made efforts to extract the basic emotions (sadness, happiness, disgust, surprise, fear, anger) conveyed through these comments. Finally, we compared the results of the five most popular classical machine learning techniques namely Naive Bayes, Decision Tree, k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and K-Means Clustering with several combinations of features. Our best model (SVM with a non-linear radial-basis function (RBF) kernel) achieved an overall average accuracy score of 52.98 and an F1 score (macro) of 0.3324
In a different paper @cite_0 , the authors described the preparation of the Bengali WordNet Affect containing six types of emotion words. They employed an automatic method of sense disambiguation. The Bengali WordNet Affect could be useful for emotion-related language processing tasks in Bengali.
@cite_0
193025648 2065586386 2250219546 1989145535
Rapid growth of blogs in the Web 2.0 and the handshaking between multilingual search and sentiment analysis motivate us to develop a blog based emotion analysis system for Bengali. The present paper describes the identification, visualization and tracking of bloggers' emotions with respect to time from Bengali blog documents. A simple pre-processing technique has been employed to retrieve and store the bloggers' comments on specific topics. The assignment of Ekman's six basic emotions to the bloggers' comments is carried out at word, sentence and paragraph level granularities using the Bengali WordNet AffectLists. The evaluation produces the precision, recall and F-Score of 59.36 , 64.98 and 62.17 respectively for 1100 emotional comments retrieved from 20 blog documents. Each of the bloggers' emotions with respect to different timestamps is visualized by an emotion graph. The emotion graphs of 20 bloggers demonstrate that the system performs satisfactorily in case of emotion tracking. The present discussion highlights the aspects of an ongoing doctoral thesis grounded on the analysis and tracking of emotions from English and Bengali texts. Development of lexical resources and corpora meets the preliminary urgencies. The research spectrum aims to identify the evaluative emotional expressions at word, phrase, sentence, and document level granularities along with their associated holders and topics. Tracking of emotions based on topic or event was carried out by employing sense based affect scoring techniques. The labeled emotion corpora are being prepared from unlabeled examples to cope with the scarcity of emotional resources, especially for the resource constraint language like Bengali. Different unsupervised, supervised and semi-supervised strategies, adopted for coloring each outline of the research spectrum produce satisfactory outcomes The discipline where sentiment opinion emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using their cognitive knowledge. Till date, all research efforts found in sentiment lexicon literature deal mostly with English texts. In this article, we propose multiple computational techniques like, WordNet based, dictionary based, corpus based or generative approaches for generating SentiWordNet(s) for Indian languages. Currently, SentiWordNet(s) are being developed for three Indian languages: Bengali, Hindi and Telugu. An online intuitive game has been developed to create and validate the developed SentiWordNet(s) by involving Internet population. A number of automatic, semi-automatic and manual validations and evaluation methodologies have been adopted to measure the coverage and credibility of the developed SentiWordNet(s). 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.