Machine learning based aspect level sentiment analysis for Amazon products
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Machine learning based aspect level sentiment analysis for Amazon products Neha Nandal1
•
Rohit Tanwar2 • Jyoti Pruthi1
Received: 23 October 2019 / Revised: 8 February 2020 / Accepted: 13 February 2020 Korean Spatial Information Society 2020
Abstract The field of sentiment analysis is widely utilized for analyzing the text data and then extracting the sentiment component out of that. The online commercial websites generates a huge amount of textual data via customer’s reviews, comments, feedbacks and tweets every day. Aspect level analysis of this data provides a great help to retailers in better understanding of customer’s expectations and then shaping their policies accordingly. However, a number of algorithms are existing these days to do aspect level sentiment detection on specified domains, but a few consider bipolar words (words which changes polarity according to context) while doing analyses. In this paper, a novel approach has been presented that utilize aspect level sentiment detection, which focuses on the features of the item. The work has been implemented and tested on Amazon customer reviews (crawled data) where aspect terms are identified first for each review. The system performs pre-processing operations like stemming, tokenization, casing, stop-word removal on the dataset to extract meaningful information and finally gives a rank for its classification in negativity or positivity. Keywords Aspects Machine learning Support vector machines Sentiment analysis API crawler Bipolar words
& Neha Nandal [email protected] 1
Department of Computer Science and Technology, Manav Rachna University, Faridabad, India
2
Department of Systemics, University of Petroleum & Energy Studies, Dehradun, India
1 Introduction Sentiment analysis is one of the techniques in natural language processing which helps in identification of sentiments that can allow entrepreneurs to get information about their customers views through different online mediums like social media, surveys, e-commerce site reviews etc. This information can make one understand the reasons of product deterioration and the aspects which are affecting the same. The era of early 2000s was the time when Sentiment analysis has aggrandized. Researchers have shown high interest in the area of sentiment analysis. Aspect level sentiment analysis came into existence as a part of sentiment analysis in which the main focus remains on particular aspects of the product/data. Two terms named as ‘Polarity’ and ‘Subjectivity’ can be explored as parts of sentiment analysis. Subjectivity refers the individual’s beliefs, views or personal sentiments while polarity simply refers to the sentiments expressed in terms of positive, negative or neutral. Sentiment analysis covers the scope of working on sentence level, document level and sub-sentence level. Different types of sentiment analysis can be performed on different domains i.e. one can do Fine-grained sentiment analysis by working on polarities in range from very negative to very positive, another
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