Improved Aspect Based Sentiment Analysis with DBN-RNN model

Main Article Content

Shilpi Gupta, Niraj Singhal, Pradeep Kumar

Abstract

Mining the sentiment target included in a sentence or text is the main aim of Aspect Based Sentiment Analysis (ABSA). This task's main challenge is to efficient extraction of a specific sentiment item's sentiment polarity. This work proposes a model namely Improved ABSA with Deep Belief Network-Recurrent Neural Network (DBN-RNN), which includes 3 working phases. Processes like stemming, stop word removal, lemmatization as well and tokenization are conducted in the initial pre-processing phase. Furthermore, in the aspect sentiment extraction phase, improved aspect term extraction (I-ATE) along with cosine similarity and word co-occurrence are used to extract the complex features from the pre-processed data. In the sentiment analysis phase, a hybrid classification model named DBN-RNN is utilized to effectively categorize the sentiments as neutral, positive, and negative polarities. The performance of proposed work is evaluated in terms of different performance measures.

Article Details

Section
Articles