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Sentiment Analysis Tool for Amazon Product Reviews
Abstract
Sentiment analysis has become a vital componentof modern data analysis, particularly for businesses that relyon customer input to improve their products and services. We employ Natural Language Processing (NLP) techniques to analyze sentiment in Amazon product reviews in this study. Our major goal is to categorize the assessments based on whether they are good, negative, or neutral. We’ll use Amazon product review data, which includes a large number of reviews from various categories, such as books, electronics, and clothes.
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The introduction of the bootstrap methods by Efron (1979) enables many empirical researches, which would otherwise be difficult if not totally impossible. Nowadays, bootstrapping has become an important aspect in research. This paper reviews various ways of bootstrapping data for cross-sectional and time series samples. Various ways of bootstrapping confidence intervals for estimators, an important application of bootstrap methods, are also discussed in this paper. Several other applications of bootstrap methods are briefly mentioned preceding to the concluding remarks this paper.
Given the wealth of literature on the topic supported by solutions to practical problems, we would expect the bootstrap to be an off-the-shelf tool for signal processing problems as are maximum likelihood and least-squares methods. This is not the case, and we wonder why a signal processing practitioner would not resort to the bootstrap for inferential problems. We may attribute the situation to some confusion when the engineer attempts to discover the bootstrap paradigm in an overwhelming body of statistical literature. Our aim is to give a short tutorial of bootstrap methods supported by real-life applications. This pragmatic approach is to serve as a practical guide rather than a comprehensive treatment, which can be found elsewhere. However, for the bootstrap to be successful, we need to identify which resampling scheme is most appropriate.
Sentiment analysis can be considered a major application of machine learning, more particularly natural language processing (NLP).As there are varieties of applications, Sentiment analysis has gained a lot of attention and is one among the fastest growing research area in computer science. It is a type of data analysis which is observed from news reports, user reviews, feedbacks, social media updates etc. Responses are collected and analyzed by researchers. All sentiments can be classified into three categories-Positive, Negative and Neutral. The paper gives a detailed study of sentiment analysis. It explains the basics of sentiment analysis, its types, and different approaches of sentiment analysis. The recent tools and APIs along with various real world applications of sentiment analysis in various areas are also described briefly.
A Review on Web Scraping and its Applications
- Vidhi Singrodia
- Anirban Mitra
- Subrata Paul
Vidhi Singrodia, Anirban Mitra, Subrata Paul, ''A Review on Web Scraping and its Applications," 2019.