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.

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

Investigating Machine Learning Techniques for User Sentiment Analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Nimesh V Patel ◽  
Hitesh Chhinkaniwala
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Product Reviews ◽  
Learning Approaches ◽  
Current Trends ◽  
Learning Techniques ◽  
Benchmark Datasets

Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.

scholarly journals Sentiment Analysis on Social Media using Machine Learning Approach

2021 ◽  
Author(s):  
Erick Omuya ◽  
George Okeyo ◽  
Michael Kimwele
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Machine Learning Approach

Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

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 Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

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.

Twitter sentiment analysis for the estimation of voting intention in the 2017 Chilean elections

2020 ◽  
Vol 24 (5) ◽  
pp. 1141-1160
Author(s):  
Tomás Alegre Sepúlveda ◽  
Brian Keith Norambuena
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Traditional Methods ◽  
Actual Result ◽  
Learning Techniques ◽  
Vector Machines

In this paper, we apply sentiment analysis methods in the context of the first round of the 2017 Chilean elections. The purpose of this work is to estimate the voting intention associated with each candidate in order to contrast this with the results from classical methods (e.g., polls and surveys). The data are collected from Twitter, because of its high usage in Chile and in the sentiment analysis literature. We obtained tweets associated with the three main candidates: Sebastián Piñera (SP), Alejandro Guillier (AG) and Beatriz Sánchez (BS). For each candidate, we estimated the voting intention and compared it to the traditional methods. To do this, we first acquired the data and labeled the tweets as positive or negative. Afterward, we built a model using machine learning techniques. The classification model had an accuracy of 76.45% using support vector machines, which yielded the best model for our case. Finally, we use a formula to estimate the voting intention from the number of positive and negative tweets for each candidate. For the last period, we obtained a voting intention of 35.84% for SP, compared to a range of 34–44% according to traditional polls and 36% in the actual elections. For AG we obtained an estimate of 37%, compared with a range of 15.40% to 30.00% for traditional polls and 20.27% in the elections. For BS we obtained an estimate of 27.77%, compared with the range of 8.50% to 11.00% given by traditional polls and an actual result of 22.70% in the elections. These results are promising, in some cases providing an estimate closer to reality than traditional polls. Some differences can be explained due to the fact that some candidates have been omitted, even though they held a significant number of votes.

scholarly journals Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Jurgita Kapočiūtė-Dzikienė ◽  
Robertas Damaševičius ◽  
Marcin Woźniak
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Full Dataset ◽  
Learning Techniques ◽  
Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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