Generating Positive and Negative Sentiment Word Clouds from E-Commerce Product Reviews

2020 ◽  
Author(s):  
Shaswat Dharaiya ◽  
Bhavin Soneji ◽  
Deep Kakkad ◽  
Naren Tada
Keyword(s):  
Product Reviews ◽  
Word Clouds ◽  
Negative Sentiment ◽  
Sentiment Word

scholarly journals Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jun Feng ◽  
Cheng Gong ◽  
Xiaodong Li ◽  
Raymond Y. K. Lau
Keyword(s):  
State Of The Art ◽  
Product Reviews ◽  
Domain Specific ◽  
Product Features ◽  
Mobile Shopping ◽  
Sentiment Lexicon ◽  
The Relationship ◽  
Chinese And English ◽  
Sentiment Word

The dramatic increase in the use of smartphones has allowed people to comment on various products at any time. The analysis of the sentiment of users’ product reviews largely depends on the quality of sentiment lexicons. Thus, the generation of high-quality sentiment lexicons is a critical topic. In this paper, we propose an automatic approach for constructing a domain-specific sentiment lexicon by considering the relationship between sentiment words and product features in mobile shopping reviews. The approach first selects sentiment words and product features from original reviews and mines the relationship between them using an improved pointwise mutual information algorithm. Second, sentiment words that are related to mobile shopping are clustered into categories to form sentiment dimensions. At each sentiment dimension, each sentiment word can take the value of 0 or 1, where 1 indicates that the word belongs to a particular category whereas 0 indicates that it does not belong to that category. The generated lexicon is evaluated by constructing a sentiment classification task using several product reviews written in both Chinese and English. Two popular non-domain-specific sentiment lexicons as well as state-of-the-art machine-learning and deep-learning models are chosen as benchmarks, and the experimental results show that our sentiment lexicons outperform the benchmarks with statistically significant differences, thus proving the effectiveness of the proposed approach.

scholarly journals Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

2021 ◽  
Vol 13 (23) ◽  
pp. 13356
Author(s):  
Ioannis Politis ◽  
Georgios Georgiadis ◽  
Aristomenis Kopsacheilis ◽  
Anastasia Nikolaidou ◽  
Panagiotis Papaioannou
Keyword(s):  
Transport System ◽  
Public Transport ◽  
Latent Dirichlet Allocation ◽  
Transport Systems ◽  
Transport Sector ◽  
Operational Conditions ◽  
The Public ◽  
Public Transport System ◽  
Word Clouds ◽  
Negative Sentiment

The coronavirus pandemic has affected everyday life to a significant degree. The transport sector is no exception, with mobility restrictions and social distancing affecting the operation of transport systems. This research attempts to examine the effect of the pandemic on the users of the public transport system of London through analyzing tweets before (2019) and during (2020) the outbreak. For the needs of the research, we initially assess the sentiment expressed by users using the SentiStrength tool. In total, almost 250,000 tweets were collected and analyzed, equally distributed between the two years. Afterward, by examining the word clouds of the tweets expressing negative sentiment and by applying the latent Dirichlet allocation method, we investigate the most prevalent topics in both analysis periods. Results indicate an increase in negative sentiment on dates when stricter restrictions against the pandemic were imposed. Furthermore, topic analysis results highlight that although users focused on the operational conditions of the public transport network during the pre-pandemic period, they tend to refer more to the effect of the pandemic on public transport during the outbreak. Additionally, according to correlations between ridership data and the frequency of pandemic-related terms, we found that during 2020, public transport demand was decreased while tweets with negative sentiment were being increased at the same time.

Aspect-based Sentiment Analysis using Dependency Parsing

2022 ◽  
Vol 21 (3) ◽  
pp. 1-19
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Minimum Distance ◽  
Dependency Graph ◽  
Media Analysis ◽  
Product Reviews ◽  
Dependency Parsing ◽  
Social Media Analysis ◽  
Dependency Parser ◽  
Sentiment Word

In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.

Sentiment Recognition from Bangla Text

2013 ◽  
pp. 315-327 ◽  
Author(s):  
K. M. Azharul Hasan ◽  
Sajidul Islam ◽  
G. M. Mashrur-E-Elahi ◽  
Mohammad Navid Izhar
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Real Life ◽  
Research Problem ◽  
Product Reviews ◽  
Important Research ◽  
Sentiment Orientation ◽  
Negative Sentiment ◽  
Source Of Information ◽  
The Web

Sentiment analysis is a very important area of the natural language processing. In general, sentiment classification means the analysis to determine the expression of a speaker whether he or she holds positive or negative opinion to a specific subject. With the rapid growth of e-commerce, sentiment analysis can greatly influence everyone in their real life. For example, product reviews on the Web have become an important source of information for customers’ decision making when they want to buy any product. As the reviews are often too many for customers to go through, how to automatically classify and detect the sentiment from them has become an important research problem. In this chapter, the authors present a Sentiment Analyzer that recognizes the Bangla sentiment or opinion about a subject from Bangla text. They construct some phrase patterns and calculate their sentiment orientation. They add tags to words in the Bangla text to construct the phrase pattern for positive and negative sentiment. Then the authors match the phrase pattern in Bangla text with their predefined phrase pattern and cumulate the sentiment orientation of each sentence.

scholarly journals A Data Processing Method Based on Sequence Labelling and Syntactic Analysis for Extracting New Sentiment Words from Product Reviews

2021 ◽  
Author(s):  
Shunxiang Zhang ◽  
Han qing Xu ◽  
Guang li Zhu ◽  
Xiang Chen ◽  
Kuang Ching Li
Keyword(s):  
Data Processing ◽  
Information Service ◽  
Recall Rate ◽  
Processing Method ◽  
Syntactic Analysis ◽  
Product Reviews ◽  
Sequence Labeling ◽  
Data Processing Method ◽  
Candidate Set ◽  
Sentiment Word

Abstract New sentiment words in product reviews are valuable resources that are directly close to users. The data processing of new sentiment word extraction can provide information service better for users, and provide theoretical support for the related research of edge computing. Traditional methods for extracting new sentiment words generally ignored the context and syntactic information, which leads to the low accuracy and recall rate in the process of extracting new sentiment words. To tackle the mentioned issue, we proposed a data processing method based on sequence labeling and syntactic analysis for extracting new sentiment words from product reviews. Firstly, the probability that the new word is a sentiment word is calculated through the location rules derived from the sequence labeling result, and the candidate set of new sentiment words is obtained according to the probability. Then, the candidate set of new sentiment words is supplemented with the method of matching appositive words based on edit distance. Finally, the final set of new sentiment words is collected through fine-grained filtering, including the calculation of Point Mutual Information (PMI) and difference coefficient of positive and negative corpus (DC-PNC). The experimental results illustrate the effectiveness of new sentiment words extracted by the proposed method which can obviously improve the accuracy and recall rate of sentiment analysis.

scholarly journals Opinion-Based Co-Occurrence Network for Identifying the Most Influential Product Features

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Ashok Kumar J ◽  
◽  
Abirami S ◽  
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Strong Relationship ◽  
Business Development ◽  
Experimental Result ◽  
Product Reviews ◽  
Graph Metrics ◽  
Product Features ◽  
Positive Sentiment ◽  
Negative Sentiment

Nowadays, social networking sites such as Facebook, Twitter, LinkedIn, YouTube, and other e-commerce websites produce a large number of text reviews. These text reviews mostly describe the product features and their opinions, which are the most important to the product developers, launchers, or buyers for business development and decisionmaking processes. Therefore, we present an opinion-based co-occurrence network for product reviews. The main aim of this research is to identify the popularity of product features or popular terms, the number of connections of a term, the strong relationship between terms, grouping the product terms, and the sentiment polarity links between terms in both positive sentiment and negative sentiment. Also, we employed the Harel-Koren fast multiscale layout algorithm and CNM (Clauset-Newman-Moore) algorithm for visualizing and grouping the network. We then measured the overall graph metrics and vertex metrics to characterize the network. Additionally, the experimental result shows the ranked product features and their social strength between product features and sentiments.

scholarly journals NAÏVE BAYES AND BLACK BOX TESTING IMPLEMENTATION ON SENTIMENT ANALYSIS OF ALOE VERA PRODUCT REVIEWS

2020 ◽  
Vol 17 (2) ◽  
pp. 95-100
Author(s):  
Putri Ambarwati
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Aloe Vera ◽  
Naïve Bayes ◽  
Black Box ◽  
Product Reviews ◽  
Bayes Method ◽  
Black Box Testing ◽  
Negative Sentiment

Aloe vera soothing gel is one of the best-selling products and the most widely reviewed on the Althea Korea website. This product has been reviewed by 1,448 users on the Althea website. The result of the research can be used to minimize mistakes in product purchases. Besides, through a review of a product, the company can analyze the level of customer satisfaction and can be a suggestion for improvements in the future. Therefore, a system is needed to analyze the sentiment towards aloe vera soothing gel to determine the review as a positive or negative sentiment. The method used in this research is the Naïve Bayes method and uses the classification carried out by linguists as a reference for determining positive and negative sentiment. There are two tests carried out in this research, namely confusion matrix testing and black-box testing. The result of the confusion matrix test found an accuracy of 94.62% and the result of black-box testing showed that the output produced was by the application functionality.

scholarly journals Sentiment Analysis on Tokopedia Product Online Reviews Using Random Forest Method

2020 ◽  
Vol 202 ◽  
pp. 16006
Author(s):  
Stephenie ◽  
Budi Warsito ◽  
Alan Prahutama
Keyword(s):  
Random Forest ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Consumer Products ◽  
Classification Model ◽  
Product Reviews ◽  
Validation Data ◽  
Random Forest Method ◽  
Sentiment Score ◽  
Negative Sentiment

Tokopedia is one of the most popular e-commerce sites in Indonesia that offers consumer products from various categories. In each product section, a review feature is offered. This review feature became essential in evaluating the sellers and become one consideration for customers in making purchase consideration. Sentiment analysis of Tokopedia product reviews may provide the opportunity to look on how Tokopedia customers respond to product quality and sellers’ hospitality. In evaluating the model, the reviews were grouped as: “positive sentiment” and “negative sentiment” using the Random Forest method and 10-fold cross-validation. Data labelling was carried out automatically by calculating the sentiment score using Lexicon-Based. Visualization of the labelling results was then done using a bar graph and a word cloud on each class of sentiment in order to look up for information that is considered important and most discussed. The test results showed that the accuracy of the Random Forest Method with parameter mtry = 73 and ntree = 50 is 97.38% which leads to the conclusion that the Random Forest Method could well predict the product reviews of Tokopedia. The greater the accuracy, the better performance of the classification model.

Mining Feature-Opinion from Reviews Based on Dependency Parsing

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1581-1591 ◽  
Author(s):  
Tieke He ◽  
Rui Hao ◽  
Hang Qi ◽  
Jia Liu ◽  
Qing Wu
Keyword(s):  
Regression Model ◽  
Opinion Mining ◽  
Experimental Results ◽  
Product Reviews ◽  
Dependency Parsing ◽  
Dependency Relationship ◽  
Feature Based ◽  
Sentiment Word ◽  
Feature Word

The manual reading of all the product reviews to find a satisfying item is not only labor-intensive, but also tedious for the consumers. In this paper, we propose a feature-opinion mining approach to automatically summarize the reviews, which is based on dependency parsing. Specifically, in our approach we first utilize a regression model to generate sentiment word, including phrase and its sentiment weight, and then we extract the feature based on the dependency relationship between feature word and sentiment word, finally we assign a score to the feature according to the dependency relationship. The experimental results demonstrate that our approach can effectively mine the feature-opinion from reviews.

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