TensSent: a tensor based sentimental word embedding method

The representation of words as vectors, conventionally known as word embeddings, has drawn considerable attention in recent years as feature learning techniques for natural language processing. The majority of these methods operate solely on the semantic and the syntactic aspects of a text, remaining oblivious to sentimental information. However, as numerous words with opposite polarities may appear in similar contexts, such as “interesting” and “boring”, the exclusive use of context-oriented information lacks the required particulars for generating word embeddings as features in sentiment analysis. Along this thread, the present study proposes two novel unsupervised models to integrating word polarity information and word co-occurrences as more tailored features for sentiment analysis. Word polarity and co-occurrence come together in the form of a tensor and tensor factorization is employed for generating the word embeddings. The experimental results on IMDB and SemEval-task2 datasets demonstrate the relatively higher performance of the proposed method compared to baseline approaches on the tasks of document-level sentiment analysis by 4.