Sentiment Classification of User Reviews Using Supervised Learning Techniques with Comparative Opinion Mining Perspective

2019 ◽  
pp. 23-29
Author(s):  
Aurangzeb Khan ◽  
Umair Younis ◽  
Alam Sher Kundi ◽  
Muhammad Zubair Asghar ◽  
Irfan Ullah ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Opinion Mining ◽  
Sentiment Classification ◽  
User Reviews ◽  
Learning Techniques ◽  
Comparative Opinion Mining

scholarly journals Semi-Supervised Target-Dependent Sentiment Classification for Micro-Blogs

2019 ◽  
Vol 19 (01) ◽  
pp. e06
Author(s):  
Shadi I. Abudalfa ◽  
Moataz A. Ahmed
Keyword(s):  
Social Media ◽  
Supervised Learning ◽  
Classification Accuracy ◽  
Opinion Mining ◽  
Semisupervised Learning ◽  
Sentiment Classification ◽  
Short Text ◽  
Learning Techniques ◽  
The Social ◽  
Learning Technique

The wealth of opinions available in the social media motivated researchers to develop automatic opinion detection tools. Many such tools are currently available online for opinion mining in short text, known as micro-blogs, but their efficacies are still limited. Current tools focus on detecting sentiment polarity expressed in a micro-blog regardless of the topic (target) discussed. Little improved approaches have been proposed to detect sentiment towards a specific target, referred to as target-dependent sentiment classification. Our literature review has shown that all these target-dependent approaches use supervised learning techniques. Such techniques need a huge amount of labeled data for increasing classification accuracy. However, preparing labeled data from social media needs a lot of efforts. In this work, we address this issue by employing semisupervised learning techniques that have not been used before with target-dependent sentiment classification. To the best of our knowledge, our work is the first research that employs semisupervised learning techniques in this direction. Semi-supervised learning techniques have been known in the literature to improve classification accuracy in comparison with supervised learning techniques; however, they use same number of labeled samples plus many unlabelled ones. In this work, we propose a new semi-supervised learning technique that uses less number of labeled microblogs than that used with supervised learning techniques. Experiment results have shown that the proposed technique provides competitive accuracy.

A gradient boosted decision tree-based sentiment classification of twitter data

2020 ◽  
Vol 18 (04) ◽  
pp. 2050027
Author(s):  
S. Neelakandan ◽  
D. Paulraj
Keyword(s):  
Decision Tree ◽  
Opinion Mining ◽  
Research Topic ◽  
Sentiment Classification ◽  
Decision Tree Classifier ◽  
Twitter Data ◽  
Tree Classifier ◽  
Boosted Decision Tree ◽  
Text Sentiment Analysis

People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users’ tweets by means of OM and automatic-SA centered on ternary classifications, namely positive, neutral and negative. It is very challenging for the researchers to ascertain sentiments as a result of its limited size, misspells, unstructured nature, abbreviations and slangs for Twitter data. This paper, with the aid of the Gradient Boosted Decision Tree classifier (GBDT), proposes an efficient SA and Sentiment Classification (SC) of Twitter data. Initially, the twitter data undergoes pre-processing. Next, the pre-processed data is processed using HDFS MapReduce. Now, the features are extracted from the processed data, and then efficient features are selected using the Improved Elephant Herd Optimization (I-EHO) technique. Now, score values are calculated for each of those chosen features and given to the classifier. At last, the GBDT classifier classifies the data as negative, positive, or neutral. Experiential results are analyzed and contrasted with the other conventional techniques to show the highest performance of the proposed method.

scholarly journals Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mita K. Dalal ◽  
Mukesh A. Zaveri
Keyword(s):  
Opinion Mining ◽  
Binary Classification ◽  
Empirical Studies ◽  
Online Reviews ◽  
Sentiment Classification ◽  
Mining System ◽  
User Reviews ◽  
Fine Grained ◽  
Feature Based ◽  
Linguistic Hedges

Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification.

scholarly journals Applying Machine Learning Techniques for Performing Comparative Opinion Mining

2020 ◽  
Vol 10 (1) ◽  
pp. 461-477
Author(s):  
Umair Younis ◽  
Muhammad Zubair Asghar ◽  
Adil Khan ◽  
Alamsher Khan ◽  
Javed Iqbal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Product Reviews ◽  
Business Organizations ◽  
Machine Learning Classifiers ◽  
Learning Techniques ◽  
Improved Accuracy ◽  
Comparative Opinion Mining

AbstractIn recent times, comparative opinion mining applications have attracted both individuals and business organizations to compare the strengths and weakness of products. Prior works on comparative opinion mining have focused on applying a single classifier, limited comparative opinion labels, and limited dataset of product reviews, resulting in degraded performance for classifying comparative reviews. In this work, we perform multi-class comparative opinion mining by applying multiple machine learning classifiers using an increased number of comparative opinion labels (9 classes) on 4 datasets of comparative product reviews. The experimental results show that Random Forest classifier has outperformed the comparing algorithms in terms of improved accuracy, precision, recall and f-measure.

Opinion Mining with SentiWordNet

2010 ◽  
pp. 266-286 ◽  
Author(s):  
Bruno Ohana ◽  
Brendan Tierney
Keyword(s):  
Opinion Mining ◽  
Sentiment Classification ◽  
The Novel ◽  
Data Set ◽  
Learning Classifier ◽  
Novel Approach ◽  
Supervised Methods ◽  
Classification Tasks ◽  
Film Reviews

Opinion Mining is an emerging field of research concerned with applying computational methods to the treatment of subjectivity in text, with a number of applications in fields such as recommendation systems, contextual advertising and business intelligence. In this chapter the authors survey the area of opinion mining and discuss the SentiWordNet lexicon of sentiment information for terms derived from WordNet. Furthermore, the results of their research in applying this lexicon to sentiment classification of film reviews along with a novel approach that leverages opinion lexicons to build a data set of features used as input to a supervised learning classifier are also presented. The results obtained are in line with other experiments based on manually built opinion lexicons with further improvements obtained by using the novel approach, and are indicative that lexicons built using semi supervised methods such as SentiWordNet can be an important resource in sentiment classification tasks. Considerations on future improvements are also presented based on a detailed analysis of classification results.

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