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
On a case study for Bengali @cite_4 , the authors considered 1,100 sentences on eight different topics. They prepared a knowledge base for emoticons and also employed a morphological analyzer to identify the lexical keywords from the Bengali WordNet Affect lists. They claimed an overall precision, recall and F1-Score (micro) of , and respectively.
@cite_4
2065586386 150652179 2152928267 2251044566
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 paper evaluates tagging techniques on a corpus of Slovene, where we are faced with a large number of possible word-class tags and only a small (hand-tagged) dataset. We report on training and testing of four different taggers on the Slovene MULTEXT-East corpus containing about 100.000 words and 1000 different morphosyntactic tags. Results show, first of all, that training times of the Maximum Entropy Tagger and the Rule Based Tagger are unacceptably long, while they are negligible for the Memory Based Taggers and the TnT tri-gram tagger. Results on a random split show that tagging accuracy varies between 86 and 89 overall, between 92 and 95 on known words and between 54 and 55 on unknown words. Best results are obtained by TnT. The paper also investigates performance in relation to our EAGLES-based morphosyntactic tagset. Here we compare the per-feature accuracy on the full tagset, and accuracies on these features when training on a reduced tagset. Results show that PoS accuracy is quite high, while accuracy on Case is lowest. Tagset reduction helps improve accuracy, but less than might be expected. In this paper we describe a database that consists of handwritten English sentences. It is based on the Lancaster-Oslo Bergen (LOB) corpus. This corpus is a collection of texts that comprise about one million word instances. The database includes 1,066 forms produced by approximately 400 different writers. A total of 82,227 word instances out of a vocabulary of 10,841 words occur in the collection. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. However, it is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used, because this knowledge can be automatically derived from the underlying corpus. The database also includes a few image-processing procedures for extracting the handwritten text from the forms and the segmentation of the text into lines and words. We present the 1.0 release of our paraphrase database, PPDB. Its English portion, PPDB:Eng, contains over 220 million paraphrase pairs, consisting of 73 million phrasal and 8 million lexical paraphrases, as well as 140 million paraphrase patterns, which capture many meaning-preserving syntactic transformations. The paraphrases are extracted from bilingual parallel corpora totaling over 100 million sentence pairs and over 2 billion English words. We also release PPDB:Spa, a collection of 196 million Spanish paraphrases. Each paraphrase pair in PPDB contains a set of associated scores, including paraphrase probabilities derived from the bitext data and a variety of monolingual distributional similarity scores computed from the Google n-grams and the Annotated Gigaword corpus. Our release includes pruning tools that allow users to determine their own precision recall tradeoff.