Opinion Mining

2019 ◽  
pp. 66-82 ◽  
Author(s):  
Vijender Kumar Solanki ◽  
Nguyen Ha Huy Cuong ◽  
Zonghyu (Joan) Lu
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Sentiment Analysis ◽  
Programming Languages ◽  
Opinion Mining ◽  
Support Vector ◽  
Emerging Trends ◽  
The One ◽  
The Mind ◽  
Technology Domain

The machine learning is the emerging research domain, from which number of emerging trends are available, among them opinion mining is the one technology attraction through which the we could get analysis of the interested domain or we can say about the review from the customer towards any product or we can say any upcoming trending information. These two are the emerging words and we can say it's the buzz word in the information technology. As you will see that its widely use by the corporate sector to uplift the business next level. Before two decade you will not read any words e.g., Opinion mining or Sentiment analysis, but in the last two decade these words have given a new life to information technology domain as well as to the business. The important question which runs in the mind is why use sentiment analysis or opinion mining. The information technology has given number of new programming languages, new innovation and within that the data mining has given this trends to the users. The chapter is covering the three major concept's which comes under the machine learning e.g., Decision tree, Bayesian network and Support vector machine. The chapter is describing the basic inputs, and how it helps in supporting stakeholders by adopting these technologies.

scholarly journals Feature-Based Opinion Mining and Managed Machine Learning with Sentiment Classification Models

2019 ◽  
Vol 9 (2) ◽  
pp. 3992-3998
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Data Intensive ◽  
Learning Tasks ◽  
Feature Based

Sentiment Analysis is individuals' opinions and feedbacks study towards a substance, which can be items, services, movies, people or events. The opinions are mostly expressed as remarks or reviews. With the social network, gatherings and websites, these reviews rose as a significant factor for the client’s decision to buy anything or not. These days, a vast scalable computing environment provides us with very sophisticated way of carrying out various data-intensive natural language processing (NLP) and machine-learning tasks to examine these reviews. One such example is text classification, a compelling method for predicting the clients' sentiment. In this paper, we attempt to center our work of sentiment analysis on movie review database. We look at the sentiment expression to order the extremity of the movie reviews on a size of 0(highly disliked) to 4(highly preferred) and perform feature extraction and ranking and utilize these features to prepare our multilabel classifier to group the movie review into its right rating. This paper incorporates sentiment analysis utilizing feature-based opinion mining and managed machine learning. The principle center is to decide the extremity of reviews utilizing nouns, verbs, and adjectives as opinion words. In addition, a comparative study on different classification approaches has been performed to determine the most appropriate classifier to suit our concern problem space. In our study, we utilized six distinctive machine learning algorithms – Naïve Bayes, Logistic Regression, SVM (Support Vector Machine), RF (Random Forest) KNN (K nearest neighbors) and SoftMax Regression.

scholarly journals Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study

2020 ◽  
Vol 4 (1) ◽  
pp. 11-20
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Practical Significance ◽  
Support Vector ◽  
Use Of The Internet ◽  
And Task

The increasing use of the internet enables users to share their opinion about what they like and dislike regarding products and services. For efficient decision making, there is a need to analyze these reviews. Sentiment analysis or opinion mining is commonly used to detect polarity (positive or negative) of reviews. But, it does not show the aspect or orientation of the text. In this study, state-of-art approaches based on supervised machine learning employed to perform three tasks on the dataset provided by SemEval. Tasks A and B are related to predicting the aspect of the restaurant’s reviews, whereas task C shows their polarity. Additionally, this study aims to compare the performance of two feature engineering techniques and five machine learning algorithms to evaluate their performance on a publicly available dataset named SemEval-2015 Task 12. The experimental results showed that the word2vec features when used with the support vector machine algorithm outperformed by giving 76%, 72% and 79% off overall accuracies for Task A, Task B, and Task C respectively. Our comparative study holds practical significance and can be used as a baseline study in the domain of aspect-based sentiment analysis.

scholarly journals Sentiment Analysis on E-commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination

2020 ◽  
Vol 8 (6) ◽  
pp. 2862-2867
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Classification Performance ◽  
Support Vector ◽  
Product Reviews ◽  
Feature Selection Technique ◽  
Backward Elimination

E-commerce is a website or mobile application platform that help people to buy products. Before purchasing the product, customer will decide to buy it or not by reading the review from previous buyer. There is a problem that there are a lot of review so it will take a long time for customer to read it all. This research will be using sentiment analysis method to classify the review data. Sentiment analysis or opinion mining is a machine learning approach to classify and analyse texts or documents about human’s sentiments, emotions, and opinions. In this research, sentiment analysis was used to classify product reviews from e-commerce websites into positive or negative classes. The results could be processed further and be used to summarize customers' opinions about a certain product without reading every single review. The goal of this research is to optimize classification performance by using feature selection technique. Terms Frequency-Inverse Document Frequency (TF-IDF) feature extraction, Backward Elimination feature selection, and five different classifiers (Naïve Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree, Random Forest) were used in analysing the sentiment of the reviews. In this research, the dataset used are Indonesian language and classified into two classes(positive and negative). The best accuracy is achieved by using TF-IDF, Backward Elimination and Support Vector Machine (SVM) with a score of 85.97%, which increases by 7.91% if compared to the process without feature selection. Based on the results, Backward Elimination feature selection succeeded in improving all performance for all classifiers used in this research.

scholarly journals Sentiment Analysis on Twitter Hashtag Datasets

2021 ◽  
Vol 9 (12) ◽  
pp. 278-281
Author(s):  
Ganesh K. Shinde
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Business Intelligence ◽  
Opinion Mining ◽  
Specific Area ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Machine Learning Approach ◽  
N Gram

Abstract: Sentiment Analysis has improvement in online shopping platforms, scientific surveys from political polls, business intelligence, etc. In this we trying to analyse the twitter posts about Hashtag like #MakeinIndia using Machine Learning approach. By doing opinion mining in a specific area, it is possible to identify the effect of area information in sentiment analysis. We put forth a feature vector for classifying the tweets as positive, negative and neutral. After that applied machine learning algorithms namely: MaxEnt and SVM. We utilised Unigram, Bigram and Trigram Features to generate a set of features to train a linear MaxEnt and SVM classifiers. In the end we have measured the performance of classifier in terms of overall accuracy. Keywords: Sentiment analysis, support vector machine, maximum entropy, N-gram, Machine Learning

A Novel Approach for Extraction of Distinguish Emotions for Semantic Granularity Level Sentiment Analysis in Multilingual Context

2020 ◽  
Vol 13 ◽  
Author(s):  
Venkateswarlu Naik Midde ◽  
Vasumathi D ◽  
A.P. Siva Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Semantic Level ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Multilingual Text

Introduction: Extraction of distinguishing semantic level emotions posed in multi-languages over social media is an essential task in the field of sentiment analysis or opinion mining. The extraction of emotions expressed in Dravidian or local languages combining with multi-languages over social media has become an essential challenge in the field of big data sentiment analysis. Methods: In the proposed approach, an innovative framework to recognize the sentiments of users in multi-languages or Dravidian languages text data using scientific linguistic theories has been defined. The proposed method used machine learning techniques such as naïve Bayes, support vector machine for fine-grained classification of multilingual text with help of lexicon-based features groups. Results: The results obtained by the experiments conducted on collected benchmark datasets in the proposed approach are outperformed and better in comparison with corpus-based and world level, phrase-level sentiment analysis for multilanguages text. Conclusion: Machine learning technnique SVM has outperformed for sentiment and emotion extraction.

scholarly journals An Enhanced Approach for Sentiment Analysis Using Association Rule Mining

2021 ◽  
Vol 9 (12) ◽  
pp. 913-918
Author(s):  
Abhishek Sharma
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Opinion Mining ◽  
Support Vector ◽  
Rule Mining ◽  
The People ◽  
Baseline Approach

Abstract: In today’s world social networking platforms like Facebook, YouTube, twitter etc. are a great source of communication for internet users and loaded with large number of emotions, views and opinions of the people. Sentiment analysis is the study of attitudes, emotions and opinions of the people and is also known as opinion mining. Sentiment analysis is used to find the opinion i.e. negative or positive about a particular subject. In this paper an Enhanced sentiment analysis approach is presented by using the Association rule mining i.e. Apriori and machine learning approach such as Support Vector Machine. The Enhanced approach is compared with the baseline approach, on accuracy, precision, recall, and F1-score measures. The Enhanced approach for sentiment analysis is implemented using the R programming language. The Enhanced approach shows better performance in comparison to the baseline approach. Keyword: Sentiment Analysis, Opinion Mining, Support Vector Machine, Association Rule Mining, Machine Learning

scholarly journals A prototype analysis of machine learning methodologies for sentiment analysis of social networks

2018 ◽  
Vol 7 (2.7) ◽  
pp. 963
Author(s):  
Vijay Kumar Atmakur ◽  
Dr P.Siva Kumar
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Support Vector ◽  
Learning Approaches ◽  
Social Network Data ◽  
Networking Technologies ◽  
Reference Classes ◽  
Analysis Of Social Networks

In present day’s social networking technologies are increased because of different user’s communication with each others. There are different types of networks are available in present situations like face book, twitter and LinkedIn. These are the valuable resources for data mining applications because of prevalence presents of different user’s information present in outside environment. Sentiment analysis is the process that defines attitudes, views, emotions and opinions from text, database sources and tweets. Sentiment analysis involves to categorize data based on different opinions like positive and negative or neutral reference classes. In this paper, we analyze different machine learning approaches to define sentiment analysis on social networks. This paper describes comparative analysis of existing machine learning approaches to classify text and other reference classes to evaluate different metric representations. And also this paper describes different machine learning methodologies like Naïve Bayesian, Entropy max and support vector machine (SVM) research on social network data streams. And also discuss major innovations to evaluate different procedures and challenges of analysis of sentiment or opinion mining aspects in present social networks.  

scholarly journals Sentiment Analysis of Customer Product Reviews using deep Learning and Compare with other Machine Learning Techniques

2021 ◽  
Vol 9 (VII) ◽  
pp. 233-239
Author(s):  
Amit Purohit
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Product Reviews ◽  
Product Analysis ◽  
Learning Techniques ◽  
Machine Learning Approach

Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. The process of finding user Opinion about the topic or Product or problem is called as opinion mining. Analyzing the emotions from the extracted Opinions are defined as Sentiment Analysis. The goal of opinion mining and Sentiment Analysis is to make computer able to recognize and express emotion. Using social media, E-commerce website, movies reviews such as Face book, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. Sentiment analysis in a machine learning approach in which machines classify and analyze the human’s sentiments, emotions, opinions etc. about the products. Out of the various classification models, Naïve Bayes, Support Vector Machine (SVM) and Decision Tree are used maximum times for the product analysis. The proposed approach will do better result as compare to other machine learning techniques.

scholarly journals Word Embedding and Feature Reduction for Sentiment Analysis Using GA

2020 ◽  
pp. 111-117
Author(s):  
Prajakta P. Shelke ◽  
Ankita N. Korde
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Feature Reduction ◽  
Word Embedding ◽  
Support Vector ◽  
Svm Classifier

Sentiment analysis (SA), also called as opinion mining is the technique for the removal of opinions of a specific entity or feature from reviews dataset. The opinions of other users help in decision making process of people. This paper studies different methods that are aimed at SA. These approaches vary from semantic based methods, machine learning, neural networks, syntactical methods with each having its own strength. Although hybrid approach also exists where the idea is to combine strengths of two or more methods to increase the accuracy. A framework in which sentiment analysis is done by using word embedding and feature reduction techniques is also proposed. Word embedding is a technique in which low-dimensional vector representation of words is provided. Feature reduction method is used with Support Vector Machine (SVM) classifier. The framework will perform sentiment analysis of user opinions by using a machine learning approach and provides a recommendation system for the ease of decision making for users. The proposed system in this paper has solved the scalability problem and improved the accuracy.

scholarly journals SENTIMENT ANALYSIS OF ENGLISH TWEETS USING BIGRAM COLLOCATION

2021 ◽  
pp. 220-227
Author(s):  
Sumaya Ishrat Moyeen ◽  
Md. Sadiqur Rahman Mabud ◽  
Zannatun Nayem ◽  
Md. Al Mamun
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Maximum Entropy ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Daily Life ◽  
Naïve Bayes ◽  
Support Vector ◽  
Huge Amount

Community and portal websites like Twitter, Facebook, Tumbler, Instagram, and LinkedIn etc. have significant impact in our day-to-day life. One of the most popular micro-blogging platforms is twitter that can provide a huge amount of data which in future can be used for various applications of opinion mining like predictions, reviews, elections, marketing etc. The users use this platform to share their views, express sentiments on various events of their daily life. Previously, many researchers have worked with twitter sentiment analysis and compared various classifiers and got the accuracy below 82%. In this work for classifying tweets into sentiments, we have used various classifiers such as Naïve Bayes, Support Vector Machine and Maximum Entropy that segregate the positive and negative tweets. Using Bigram Collocation with classifiers, we’ve acquired 88.42% accuracy. KEYWORDS: Twitter; Sentiment Classification; Machine Learning; NLTK; Python; Naïve Bayes; Support Vector Machine (SVM); Maximum Entropy

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