A Study on Classification Methods Applied to Sentiment Analysis

2013 ◽  
Author(s):  
Valentina Mazzonello ◽  
Salvatore Gaglio ◽  
Agnese Augello ◽  
Giovanni Pilato
Keyword(s):  
Sentiment Analysis ◽  
Classification Methods

A Multiple-Layer Machine Learning Architecture for Improved Accuracy in Sentiment Analysis

2019 ◽  
Vol 63 (3) ◽  
pp. 395-409 ◽  
Author(s):  
L B Shyamasundar ◽  
P Jhansi Rani
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classification Methods ◽  
Evaluation Standard ◽  
Learning Classifier ◽  
Scaling Methods ◽  
Chi Squared ◽  
Value Decomposition ◽  
Improved Accuracy

Abstract Twitter is an online micro-blogging platform through which one can explore the hidden valuable and delightful information about the current context at any point of time, which also serves as a data source to carry out sentiment analysis. In this paper, the sentiments of large amount of tweets generated from Twitter in the form of big data have been analyzed using machine learning algorithms. A multi-tier architecture for sentiment classification is proposed in this paper, which includes modules such as tokenization, data cleaning, preprocessing, stemming, updated lexicon, stopwords and emoticon dictionaries, feature selection and machine learning classifier. Unigram and bigrams have been used as feature extractors together with χ2 (Chi-squared) and Singular Value Decomposition for dimensionality reduction together with two model types (Binary and Reg), with four types of scaling methods (No scaling, Standard, Signed and Unsigned) and represented them in three different vector formats (TF-IDF, Binary and Int). Accuracy is considered as the evaluation standard for random forest and bagged trees classification methods. Sentiments were analyzed through tokenization and having several stages of pre-processing and several combinations of feature vectors and classification methods. Through which it was possible to achieve an accuracy of 84.14%. Obtained results conclude that, the proposed scheme gives a better accuracy when compared with existing schemes in the literature.

scholarly journals Collaborative Classification Approach for Airline Tweets Using Sentiment Analysis

2021 ◽  
Vol 12 (3) ◽  
pp. 3597-3603
Author(s):  
M.Veera Kumari Et.al
Keyword(s):  
Sentiment Analysis ◽  
Cross Validation ◽  
Majority Voting ◽  
Quality Of Services ◽  
Classification Methods ◽  
K Nearest Neighbors ◽  
Classification Techniques ◽  
Improve Accuracy ◽  
Fold Cross Validation

In the world there are so many airline services which facilitate different airline facilities for their customers. Those airline services may satisfy or may not satisfy their customers. Customers cannot express their comments immediately, so airline services provide the twitter blog to give the feedback on their services. Twitter has been increased to develop the quality of services[4]. This paper develop the different classification techniques to improve accuracy for sentiment analysis. The tweets of services are classified into three polarities such as positive, negative and neutral. Classification methods are Random forest(RF), Logistic Regression(LR), K-Nearest Neighbors(KNN), Naïve Baye’s(NB), Decision Tree(DTC), Extreme Gradient Boost(XGB), merging of (two, three and four) classification techniques with majority Voting Classifier, AdaBoost measuring the accuracy achieved by the function using 20-fold and 30-fold cross validation was compassed in the validation phase. In this paper proposes a new ensemble Bagging approach for different classifiers[10]. The metrics of sentiment analysis precision, recall, f1-score, micro average, macro average and accuracy are discovered for all above mentioned classification techniques. In addition average predictions of classifiers and also accuracy of average predictions of classifiers was calculated for getting good quality of services. The result describes that bagging classifiers achieve better accuracy than non-bagging classifiers.

scholarly journals Arabic Sentiment Analysis: A Systematic Literature Review

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Abdullatif Ghallab ◽  
Abdulqader Mohsen ◽  
Yousef Ali
Keyword(s):  
Systematic Review ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Sentiment Classification ◽  
Future Research ◽  
Classification Methods ◽  
Feature Generation ◽  
Future Studies ◽  
Arabic Sentiment Analysis ◽  
Support Research

With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

scholarly journals Linking Twitter Sentiment Knowledge with Infrastructure Development

MATEMATIKA ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 91-102 ◽  
Author(s):  
Zakya Reyhana ◽  
Kartika Fithriasari ◽  
Moh. Atok ◽  
Nur Iriawan
Keyword(s):  
Neural Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Automatic Extraction ◽  
Infrastructure Development ◽  
Classification Methods ◽  
Negative Comments ◽  
Text Mining Application ◽  
Better Than

Sentiment analysis is related to the automatic extraction of positive or negative opinions from the text. It is a special text mining application. It is important to classify implicit contents from citizen’s tweet using sentiment analysis. This research aimed to find out the opinion of infrastructure that sustained urban development in Surabaya, Indonesia’s second largest city. The procedures of text mining analysis were the data undergoes some preprocessing first, such as removing the link, retweet (RT), username, punctuation, digits, stopwords, case folding, and tokenizing. Then, the opinion was classified into positive and negative comments. Classification methods used in this research were support vector machine (SVM) and neural network (NN). The result of this research showed that NN classification method was better than SVM.

scholarly journals COVID19 Sentiment Analysis using Machine Learning Classification Algorithms

2021 ◽  
Vol 7 (09) ◽  
pp. 13-18
Author(s):  
Kusumanchi Naga Sireesha and Padala Srinivasa Reddy
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Machine Learning Classification ◽  
Inaccurate Information ◽  
Public Sentiment ◽  
Analysis System

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis. The diverse use of social networking sites, like Twitter, speeds up the process of sharing information and having views on community events and health crises COVID-19 has been one of Twitter's trending areas. The Twitter messages created via Twitter are named Tweets. In this paper, we identify public sentiment associated with the pandemic using Coronavirus-specific Tweets and Python, along with its sentiment analysis packages. We provide an overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. This research provides insights into Coronavirus fear sentiment progression, associated methods, limitations, and different opportunities. In this project, we have designed a Sentiment analysis System that would identify the sentiment of a tweet and classify it into one of the five classes they include:”ExtremelyPositive”,“Positive”,”Neutral”, ”Negative” and “Extremely Negative”.

scholarly journals Using Feature Extraction and Classification Methods of Movie Opinions Predication

2019 ◽  
Vol 9 (2) ◽  
pp. 1519-1522
Keyword(s):  
Feature Extraction ◽  
Sentiment Analysis ◽  
Classification Methods ◽  
Selection Technique ◽  
Methods Of Analysis ◽  
Feature Extraction And Selection ◽  
Picture Show

Film rankings and analysis at sites like IMDb (Internet Movie Database) square measure ordinarily employed by picture show goers to make your mind up that movie to look at or obtain next. Currently, picture show goers base their choices on that movie to look at by staring at the ratings of films in addition as reading a number of the reviews at IMDB. Sentiment analysis could be a different field of different opinion where the methods of analysis are targeted on feature extraction and selection technique of emotions and opinions of the individual’s audience towards selected methods from semi-structured, structured or unstructured matter information. This paper, we focus on our techniques of sentimental analysis on IMDB picture show review information. To survey the sentimental words method to classify the polarity of the picture show review on a scale of highly dislikes highly liking and performing different extraction feature and positioning of reviews. It uses these options to train our multilable classifier to classify the picture show review into its correctable.

A Survey on Implementation Methods and Applications of Sentiment Analysis

2019 ◽  
pp. 44-58
Author(s):  
Sudheer Karnam ◽  
Valarmathi B. ◽  
Tulasi Prasad Sariki
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Mobile Phones ◽  
Opinion Mining ◽  
Classification Methods ◽  
Analysis Problem ◽  
Public Opinions ◽  
The Public ◽  
Research Problems

Sentiment analysis also called opinion mining, and it studies opinions of people towards products and services. Opinions are very important as the organizations always want to know the public opinions about their products and services. People give their opinions via social media. With the advent of social media like Twitter, Facebook, blogs, forums, etc. sentiment analysis has become important in every field like automobile, medical, film, fashion, stock market, mobile phones, insurance, etc. Analyzing the opinions and predicting the opinion is called sentiment analysis. Sentiment analysis is done using opinion words by classification methods or by sentiment lexicons. This chapter compares different methods of solving sentiment analysis problem, algorithms, its merits and demerits, applications, and also investigates different research problems in sentiment analysis.

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