scholarly journals Aspect Based Sentiment Analysis for E-Commerce Shopping Website

2021 ◽  
Vol 9 (11) ◽  
pp. 1819-1822
Author(s):  
Neha V. Thakare
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Emotion Detection ◽  
Research Knowledge ◽  
Text Data ◽  
Customer Expectations ◽  
Customer Reviews ◽  
Classification Feature ◽  
On Line ◽  
Area Unit

Abstract: Sentiment Analysis is that the most ordinarily used approach to research knowledge that is within the form of text and to identify sentiment content from the text. Opinion Mining is another name for sentiment analysis. a good vary of text data is getting generated within the form of suggestions, feedback, tweets, and comments. E-Commerce portals area unit generating tons of data. Every day within the form of customer reviews. Analyzing E-Commerce data can facilitate on-line retailers to grasp customer expectations, offer an improved searching expertise, and to extend sales. Sentiment Analysis can be used to identify positive, negative, and neutral information from the customer reviews. Researchers have developed a lot of techniques in Sentiment Analysis. Keywords: Sentiment analysis, Sentiment classification, Feature selection, Emotion detection, Customer Reviews;

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

scholarly journals Sarcasm Detection in Text Data Using Glove Embedding

2021 ◽  
Vol 9 (8) ◽  
pp. 2495-2499
Author(s):  
Samrudhi Naik
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Processing Technique ◽  
Text Data ◽  
Natural Language Processing Technique

Abstract: Sarcasm is a way of expressing feelings in which people say or write something which is completely different or opposite to what they actually mean to say. Hence it is very difficult to identify sarcasm . It is usually an ironic or satirical remark tempered by humor. Mainly, people use it to say the opposite of what's true to make someone look or feel foolish. Understanding the sarcasm can improve the accuracy of sentiment analysis. Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. This helps in identifying what the opinions of users or individual or society are. In this project an attempt is made to develop a model to detect if a sentence is sarcastic or if it is not sarcastic. Keywords: Sarcasm detection, GloVe Embedding, LSTM, Natural Language Processing, Sentiment

scholarly journals An Approach To Twitter Sentiment Analysis Over Hadoop

2018 ◽  
Vol 7 (4.5) ◽  
pp. 374
Author(s):  
Yazala Ritika Siril Paul ◽  
Dilipkumar A. Borikar
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Emotional State ◽  
Streaming Data ◽  
Data Streaming ◽  
Text Data ◽  
Twitter Data ◽  
The People ◽  
Data Platform ◽  
Apache Hive

Sentiment analysis is the process of identifying people’s attitude and emotional state from the language they use via any social websites or other sources. The main aim is to identify a set of potential features in the review and extract the opinion expressions of those features by making full use of their associations. The Twitter has now become a routine for the people around the world to post thousands of reactions and opinions on every topic, every second of every single day. It’s like one big psychological database that’s constantly being updated and which can be used to analyze the sentiments of the people. Hadoop is one of the best options available for twitter data sentiment analysis and which also works for the distributed big data, streaming data, text data etc.  This paper provides an efficient mechanism to perform sentiment analysis/ opinion mining on Twitter data over Hortonworks Data platform, which provides Hadoop on Windows, with the assistance of Apache Flume, Apache HDFS and Apache Hive. 

Sentimental Analysis of Online Reviews Using Fuzzy Sets and Rough Sets

2017 ◽  
pp. 376-394
Author(s):  
Anuradha Jagadeesan ◽  
Amit Patil
Keyword(s):  
Fuzzy Sets ◽  
Sentiment Analysis ◽  
Rough Sets ◽  
Opinion Mining ◽  
Online Reviews ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Excellent Source ◽  
The Web

With the increased interest of online users in E-commerce, the web has become an excellent source for buying and selling of products online. Customer reviews on the web help potential customers to make purchase decisions, and for manufacturers to incorporate improvements in their product or develop new marketing strategies. The increase in customer reviews of a product influence the popularity and the sale rate of the product. This lead to a very important question about the analysis of the sentiments (opinions) expressed in the reviews. As such internet does not have any quality control over customer reviews and it could vary in terms of its quality. Also the trustworthiness of the online reviews is debatable. Sentiment Analysis (SA) or Opinion Mining is the computational analysis of opinions, sentiments, emotions and subjectivity of text. In this chapter, we take a look at the various research challenges and a new dimension involved in sentiment analysis using fuzzy sets and rough sets.

scholarly journals Opinion Mining and Sentiment Analysis: A Survey

2012 ◽  
Vol 2 (3) ◽  
pp. 171-178 ◽  
Author(s):  
Mohammad Sadegh Hajmohammadi ◽  
Roliana Ibrahim ◽  
Zulaiha Ali Othman
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Time Machine ◽  
Web Documents ◽  
Customer Reviews ◽  
The Past ◽  
New Research ◽  
Machine Readable ◽  
Research Domain

In the past few years, a great attention has been received by web documents as a new source of individual opinions and experience. This situation is producing increasing interest in methods for automatically extracting and analyzing individual opinion from web documents such as customer reviews, weblogs and comments on news. This increase was due to the easy accessibility of documents on the web, as well as the fact that all these were already machine-readable on gaining. At the same time, Machine Learning methods in Natural Language Processing (NLP) and Information Retrieval were considerably increased development of practical methods, making these widely available corpora. Recently, many researchers have focused on this area. They are trying to fetch opinion information and analyze it automatically with computers. This new research domain is usually called Opinion Mining and Sentiment Analysis. . Until now, researchers have developed several techniques to the solution of the problem. This paper try to cover some techniques and approaches that be used in this area.

scholarly journals Current Apprises of Opinion Mining Methods

2019 ◽  
Vol 9 (2) ◽  
pp. 3511-3516
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Storage Capacity ◽  
Development Research ◽  
Emotion Detection ◽  
Accurate Picture ◽  
Global Companies ◽  
Crime Detection ◽  
And Storage ◽  
Day By Day

increasingly, the data is increasing day by day and storage capacity is expanding more and more, this allowing the field of SA to growing and developing faster in research and prospecting for different opinions and emotions to be combed and technically treated to be more accurate. In our present, data can be a wealth where major global companies and development, research and crime detection centers benefit from it. In this paper we focused on the current apprises of research in this field which contributed to various improvements in the field of sentiment analysis. We have tackles comprehensive overviews for different fields which related to the Sentiment Analysis (Transfer Learning (TL), Building Resource (BR), Emotion Detection (ED)) which have the popularity of researchers has gained in recent times and attracted them. We have the aim of this survey which is to give a clear and accurate picture about the techniques of analyzing emotions and related fields

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

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