SENTIMENT ANALYSIS ON REVIEWS OF WOMEN'S TOPS ON SHOPEE MARKETPLACE USING NAIVE BAYES ALGORITHM

Reviews of women's tops in the market are valuable information if processed properly. Merchants can conduct product review analysis to obtain information that can be used to evaluate products and services. Product review analysis activities are not enough just to see the number of stars, it is necessary to see the entire content of the review comments to be able to know the intent of the review. Sentiment analysis system is a system used to automatically analyze online product reviews to obtain information including sentiment information that is part of online reviews. The data is classified using Naive Bayes. The data collected were 1,000 product reviews of women's tops as samples.  The purpose of this study is to determine the sentiment analysis of female top product reviews using the Naive Bayes algorithm. The stages of this research include data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning emoticons & symbols, Case folding, Word Normalizer, Tokenize, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be classified into 3 (three) classes, namely negative, positive and neutral. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 89%, this result shows that the product reviews of women's tops are more positive

Deep Learning Implementation for Comparison of User Reviews and Ratings

Sentiment Analysis is the task of identifying and classifying the sentiment expressed in a piece of text as of positive or negative sentiment and has wide application in E-Commerce. In present time, most e-commerce websites have product review sections, which can be used to identify customer satisfaction/dissatisfaction for their product. In E-COMMERCE websites such as Amazon.com, E-bay.com etc, consumers can submit their reviews along with a specific polarity rating (e.g. 1 to 5 stars at Amazon.com). There is a possibility of mismatch between review submitted and polarity of rating. For Amazon.com, a customer can submit a strongly positive review but give it a low rating. The objective of this thesis is to develop a web-service application which can be used to tackle this situation. We will perform Sentiment Analysis using Deep Learning on Amazon.com product review data. Product reviews will be converted to vectors using “PARAGRAPH VECTOR” which will later be used to train a Recurrent Neural Network with Gated Recurrent Unit. Our model will incorporate both semantic relationship of review text as well as product information. We have also devel- oped an application in Python, that will predict rating score for the submitted review using the trained model. If there is a mismatch between predicted rating score and submitted rating score, a warning/info will be provided

Identifying the Conditions under Which Online Reviews Translate into Product Sales: A Sentiment Analysis Approach

Number of available online consumer reviews has increased dramatically. This research-in-progress study draws on Cue-Summation and Communication Persuasion theories to define and validate the online review positiveness as a multidimensional formative index and conduct a sentiment analysis, using secondary data from Amazon.com, to identify the conditions under which online reviews translate into product sales. Particularly, this study focus on the role of product involvement, review time, product price, and review score inconsistency on the impact of online review positiveness on product sales. Potential contributions to theory and practice are also discussed

Context Driven Bipolar Adjustment for Optimized Aspect Level Sentiment Analysis

122–127World Wide Web provides numerous opinionated data that can influence users. Reviews on online data highly affect the user’s perception while buying a particular or related product from an online shopping site. The online review provided by a customer helps other customers to make up their decision regarding purchasing that item. Looking at the developer’s and producer’s perspective, the opinions of customers on their manufactured items is helpful in identifying deformities as well as scope for improving its quality. Equipped with all this information, the product can be developed and managed more efficiently. Along with the overall rating of the product, the feature-based rating will have a great impact on the decision-making process of the customer. In this paper, an optimized scheme of aspect level sentiment analysis is presented to analyze the online reviews of a product. Reviews ratings have been used for learning approach. Inherently biased reviews are considered to optimize the Aspect Level Sentiment Analysis. Bi-polar aspect level sentiment analysis model has been trained using multiple kernels of support vector machine to optimize the results. Lexicon based aspect level sentiment analysis is performed first and later on the basis of bipolar words adjustment, and its effect on results, aspect level sentiment analysis for efficient optimization has been performed. A Web Crawler is developed to extract data from Amazon. The results obtained outperformed traditional lexicon based Aspect Level Sentiment Analysis

Hybrid Sentiment Classification of Reviews Using Synonym Lexicon and Word embedding

Sentiment analysis is used in extract some useful information from the given set of documents by using Natural Language Processing (NLP) techniques. These techniques have wide scope in various fields which are dealing with huge amount of data link e-commerce, business and market analysis, social media and review impact of products and movies. Sentiment analysis can be applied over these data for finding the polarity of the data like positive, neutral or negative automatically or many complex sentiments like happiness, sad, anger, joy, etc. for a particular product and services based on user reviews. Sentiment analysis not only able to find the polarity of the reviews. Sentiment analysis utilizes machine learning algorithms with vectorization techniques based on textual documents to train the classifier models. These models are later used to perform sentiment analysis on the given dataset of particular domain on which the classifier model is trained. Vectorization is done for text document by using word embedding based and hybrid vectorization. The proposed methodology focus on fast and accurate sentiment prediction with higher confidence value over the dataset in both Tamil and English

Sentiment analysis of health care tweets: review of the methods used.

BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first

Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network

Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%

A Hybrid Model for Sentiment Analysis Based on Movie Review Datasets

The classification of sentiments, often known as sentiment analysis, is now widely recognized as an open field of research. Over the past few years, a huge amount of study work has been carried out in these disciplines by utilizing a wide variety of research approaches. Due to the possibility that the performance of sentiment analysis may be impacted by the high-dimensional feature set, text mining demands careful consideration during in the construction and selection of features.The process of recognising and extracting subjective information from written data is referred to as sentiment analysis. Sentiment analysis enables companies to understand the social sentiment around their brand, product, or service by monitoring the conversations that take place in internet chat rooms. In order to categorise people's attitudes or sentiments, this study provides a hybrid model (Support Vector Machine, Convolutional Neural Network, and Long Short-Term Memory). The findings of using the network model to sentiment analysis on the movie review or amazon review datasets reveal that it is possible to gain a good classification impact by using the model. The preprocessing is used for text mining, the removal of punctuation, and the generation of vocabulary, also uses GLOVE for vectorization and TF-IDF algorithms for better feature extraction.  The results that were proposed were compared with various base models such as KNN, and MNB, amongst others, which demonstrates that the hybrid model performs better than other models