Journal ArticleDOI
Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks
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TLDR
The empirical results indicate that the proposed deep learning architecture outperforms the conventional deep learning methods on sentiment analysis on product reviews obtained from Twitter.Abstract:
Sentiment analysis is one of the major tasks of natural language processing, in which attitudes, thoughts, opinions, or judgments toward a particular subject has been extracted. Web is an unstructured and rich source of information containing many text documents with opinions and reviews. The recognition of sentiment can be helpful for individual decision makers, business organizations, and governments. In this article, we present a deep learning‐based approach to sentiment analysis on product reviews obtained from Twitter. The presented architecture combines TF‐IDF weighted Glove word embedding with CNN‐LSTM architecture. The CNN‐LSTM architecture consists of five layers, that is, weighted embedding layer, convolution layer (where, 1‐g, 2‐g, and 3‐g convolutions have been employed), max‐pooling layer, followed by LSTM, and dense layer. In the empirical analysis, the predictive performance of different word embedding schemes (ie, word2vec, fastText, GloVe, LDA2vec, and DOC2vec) with several weighting functions (ie, inverse document frequency, TF‐IDF, and smoothed inverse document frequency function) have been evaluated in conjunction with conventional deep neural network architectures. The empirical results indicate that the proposed deep learning architecture outperforms the conventional deep learning methods.read more
Citations
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A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews
TL;DR: In this paper, the authors present a study to determine the usefulness, scope, and applicability of this alliance of ML techniques for consumer sentiment analysis (CSA) for online reviews in the domain of hospitality and tourism.
Posted Content
A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews
TL;DR: The study is presented to find out the usefulness, scope, and applicability of this alliance of Machine Learning techniques for consumer sentiment analysis on online reviews in the domain of hospitality and tourism.
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