Sentiment Analysis Using Smoothed Probabilistic-Based Models

In this paper, we propose an unsupervised learning algorithm for Natural Language Processing (NLP). In particular, we present a sentiment analysis solution using probabilistic models. We propose two new smoothed probabilistic-based models that incorporate the benefits of smoothing techniques and scaling word vectors to address sparseness and high-dimensionality challenges. We introduce the smoothed scaled Dirichlet and the smoothed shifted scaled Dirichlet mixture models, the learning approach for the mixture parameters, the clustering algorithms, and the sentiment analysis framework. We consider in our experiments different benchmarks of sentiment analysis, namely, Stanford Twitter sentiment (STD), Stanford sentiment gold standard (STS-Gold), SemEval2014 Task9, and Sentiment Strength Twitter (SentiStrength). The results are compared with the baselines and state-of-the-art (SOTA) models in the literature where our proposed approaches outperform all the other models.