Shapan Das Uzzal’s scientific contributions

The vector representation of Bengali words using word2vec model (Mikolov et al. (2013)) plays an important role in Bengali sentiment classification. It is observed that the words that are from same context stay closer in the vector space of word2vec model and they are more similar than other words. In this article, a new approach of sentiment classification of Bengali comments with word2vec and Sentiment extraction of words are presented. Combining the results of word2vec word co-occurrence score with the sentiment polarity score of the words, the accuracy obtained is 75.5%.

Sentiment Analysis is one of the most important and challenging research topic in the field of natural language processing and opinion mining. In this article, six different approaches are discussed to determine the actual sentiment of the sentence and analyzed their performances. In parts of speech ratio method, the Parts of Speech (POS) of the queries are tagged and the POS ratio and the hamming distance between positive classifier and query and negative classifier and query are computed. To detect the sentiment more accurately, cosine similarity using TF-IDF is applied which is calculated by computing TF, DF and IDF and calculate positive vector, negative vector and query vector. In Cosine similarity using custom TF-IDF, custom POS tagger is used and TF, DF and IDF are computed. Another method with Naïve Bayes model using Uni-gram & stammer also gives good performance. In this approach, prior probability and conditional probability are calculated and the root words of the words are extracted. Naïve Bayes model using Bi-gram, stammer and normalizer is better than the other models. The last method discussed is Word Embedding with Hellinger PCA which presents the idea of word co-occurrence matrix and Skip-Gram to determine the actual contexts of the words, Hellinger PCA to determine most similar words and generate a sliding window of most probable context words around each word.

The sentiment of a sentence or a comment can be detected more accurately by applying Word Embeddings. This article presents the idea of word co-occurrence matrix and Skip-Gram to determine the actual contexts of the words, Hellinger PCA to determine the most similar words and generate a sliding window of most probable context words around each word. It is shown that, by applying Word Embeddings to classify the sentiment of a comment achieves higher accuracy with larger corpus. For our corpus of 2500 comments, the accuracy achieved is 70%, which is rapidly increasing with the size of the corpus.