Sentiment Weighted Word Embedding for Big Text Data

Jenish Dhanani,Dipti Rana,Rupa Mehta

doi:10.4018/ijwltt.20211101.oa2

Abstract

Sentiment analysis is the practice of eliciting a sentiment orientation of people's opinions (i.e. positive, negative and neutral) toward the specific entity. Word embedding technique like Word2vec is an effective approach to encode text data into real-valued semantic feature vectors. However, it fails to preserve sentiment information that results in performance deterioration for sentiment analysis. Additionally, big sized textual data consisting of large vocabulary and its associated feature vectors demands huge memory and computing power. To overcome these challenges, this research proposed a MapReduce based Sentiment weighted Word2Vec (MSW2V), which learns the sentiment and semantic feature vectors using sentiment dictionary and big textual data in a distributed MapReduce environment, where memory and computing power of multiple computing nodes are integrated to accomplish the huge resource demand. Experimental results demonstrate the outperforming performance of the MSW2V compared to the existing distributed and non-distributed approaches.

Highlights

With the advancement of Internet technologies, platforms like Social Media, E-Commerce and Movie Streaming Services have directly reached the millions of individuals
The proposed MapReducebased sentiment-weighted Word2Vec (MSW2V) learns the local sentiment weighted feature vector by assigning the Sentiment Polarity Scores (SPSs) to the local Word2vec embedding on multiple computing nodes of Map Phase
In shallow Neural Network (NN) training, the Continuous Bag of Word (CBOW) architecture puts the current word on the input layer, and company words of the current word on the output layer to predict the current word based on the company words

Summary

INTRODUCTION

With the advancement of Internet technologies, platforms like Social Media, E-Commerce and Movie Streaming Services have directly reached the millions of individuals. Existing sentiment analysis approaches learn sentiment and semantic specific feature vectors for big sized textual data, which possess the scalability issue (Dhanani et al, 2018; Parikh et al, 2018). To overcome these challenges, this research proposes a novel sentiment weighted word embedding approach using sentiment dictionary and distributed MapReduce environment. The proposed MSW2V learns the local sentiment weighted feature vector by assigning the SPSs to the local Word2vec embedding on multiple computing nodes of Map Phase It results in a clear bifurcation of semantically similar words based on sentiment values

BACKGROUND

RELATED WORKS

Result