scholarly journals A Peruse on Feature Specific: Quality Prediction of Product using Opinion Mining

2020 ◽  
Vol 8 (5) ◽  
pp. 2349-2354
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Quality Prediction ◽  
General Process ◽  
Future Work ◽  
Level Of Analysis

The field of sentiment analysis, in which the sentiments of the text are collected, analyzed and compiled, has received much attention in recent years. The corresponding growth in this area has led to the emergence of different sub-regions, each of which relates to a different level of analysis or research. This research focuses on the analysis of feelings at his level, with the aim of finding and adding feelings in the entities mentioned in the documents or aspects thereof. An in-depth overview is given of the newest current developments, illustrating the enormous progress that has already been made to find both the purpose that an entity can be as such, or some aspects of it, and the associated sentimentity. Sentiment analysis has received much attention in recent years. In this article, our goal is to address the problem of classifying the polarity of feelings, one of the fundamental problems of sentiment analysis. A general process is proposed to classify the polarity of feelings with a detailed description of the process. The data used in this study are reviews of online products collected through Amazon.com. Experiments are performed both for the classification at the prayer level and for the classification at the revision level with promising results. Finally, we also provide information about our future work in the analysis of sentiments.

scholarly journals Sentiment analysis: a challenge

2018 ◽  
Vol 7 (2.27) ◽  
pp. 291 ◽  
Author(s):  
Ekta Gupta ◽  
Ashok Kumar ◽  
Manish Kumar
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Accurate Result ◽  
Product Reviews ◽  
General Process ◽  
Sentence Level ◽  
Complete Procedure ◽  
Future Work ◽  
Online Buying

Sentiment analysis or judgment/thoughts mining is one of the major jobs of NLP (Natural Language Processing). Sentiment analysis has acquired much awareness in recent years. In this paper, our focus is to approach the problem of sentiment polarity assortment, which is one of the elementary problems of sentiment analysis. A general process for sentiment polarity assortment is considered with complete procedure explanation. Data used in this research are online buying product reviews collected from the shopping platform Amazon.com. Experiments for both sentence-level assortment and review-level assortment are executed with guarantee outcomes. Sentiment analysis will help to enhance the business with its performance of giving accurate result .In the end; we also give awareness into our future work on sentiment analysis. From last decade there is no such work has done on sentiment analysis to improve the product quality on the basis of what the customer needs and sometimes it is introduce as opinion mining while the importance in this case is on extraction.  

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.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

scholarly journals Social media and sentiment in bioenergy consultation

2016 ◽  
Vol 10 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Victoria Uren ◽  
Daniel Wright ◽  
James Scott ◽  
Yulan He ◽  
Hassan Saif
Keyword(s):  
Social Media ◽  
New Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Public Consultation ◽  
Short Paper ◽  
Consultation Process ◽  
Content Type ◽  
The Public ◽  
The Uk

Purpose – This paper aims to address the following challenge: the push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organizations towards energy development projects. Design/methodology/approach – This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised and illustrated using a sample of tweets containing the term “bioenergy”. Findings – Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications – Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Social implications – Social media have the potential to open access to the consultation process and help bioenergy companies to make use of waste for energy developments. Originality/value – Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

Sentiment Analysis of Comparative Sentences for Chinese Document

2012 ◽  
Vol 157-158 ◽  
pp. 1079-1082
Author(s):  
Guo Shi Wu ◽  
Xiao Yin Wu ◽  
Jing Jing Wei
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Sentiment Classification ◽  
The Third ◽  
Sentence Patterns ◽  
Simple Sentences ◽  
Document Level ◽  
Level Analysis

One of the most widely-studied sub-problems of opinion mining is sentiment classification, which includes three study levels: word, sentence and document. At the third level, most of the existing methods ignore comparative sentences which have particular sentence patterns and may lower the precision of the document-level analysis. This paper studies sentiment analysis of comparative sentences. The aim is to determine whether opinions expressed in a comparative sentence are positive or negative. Experiments of comparing with document-level sentiment analysis based on simple sentences shows the effectiveness of the proposed method.

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