Text Preprocessing Method on Twitter Sentiment Analysis using Machine Learning

In real world, twitter sentimental analysis (TSA) acting a major role in observing the public opinion about customer side. TSA is complex compared to general sentiment analysis due to pre-processing of text on Twitter. The maximum limit on the number of characters allowed on Twitter is 280. In this article we discuss the influence of the text pre-processing technique on the classification efficiency of emotions in two kinds of classification problems and summarize the classification efficiency of the four pre-processing methods. This paper contributes to the consumer satisfaction classification sentiment analysis and is useful in evaluating the details in the context of the amount of tweets where views are somewhat unstructured and are either positive or negative, or somewhere in between. We first pre-processed the dataset, then extracted the adjective from the dataset with some meaning called the feature vector, then selected the feature vector list and subsequently applied machine learning based classification algorithms namely: Naive Bayes, Random Forest and SVM along with WordNet based Semantic Orientation which extracts synonyms and similarity for the features of content. Experiments display that the accuracy (Acc) and average F1-measure (F1-M) of the classification classifier on Twitter are enhanced by using methods of pre-processing the extension of acronyms and swapping negation, but barely deleting numbers or stop words

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Financial Context News Sentiment Analysis for the Lithuanian Language

Applied Sciences ◽

10.3390/app11104443 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4443

Author(s):

Rokas Štrimaitis ◽

Pavel Stefanovič ◽

Simona Ramanauskaitė ◽

Asta Slotkienė

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Experimental Investigations ◽

Support Vector ◽

Applied Machine Learning ◽

Bayes Algorithm ◽

Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

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Machine Learning Approaches for Sentiment Analysis

Data Mining and Analysis in the Engineering Field - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-6086-1.ch011 ◽

2014 ◽

pp. 193-208 ◽

Cited By ~ 9

Author(s):

Basant Agarwal ◽

Namita Mittal

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Opinion Mining ◽

Machine Learning Algorithms ◽

Sentiment Classification ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Knowledge Based ◽

Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

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Machine Learning Approaches for Sentiment Analysis

Big Data ◽

10.4018/978-1-4666-9840-6.ch088 ◽

2016 ◽

pp. 1917-1933

Author(s):

Basant Agarwal ◽

Namita Mittal

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Opinion Mining ◽

Machine Learning Algorithms ◽

Sentiment Classification ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Knowledge Based ◽

Semantic Orientation

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Impact of Deep Learning on Semantic Sentiment Analysis

Examining the Impact of Deep Learning and IoT on Multi-Industry Applications - Advances in Web Technologies and Engineering ◽

10.4018/978-1-7998-7511-6.ch007 ◽

2021 ◽

pp. 97-117

Author(s):

Neha Gupta ◽

Rashmi Agrawal

Keyword(s):

Machine Learning ◽

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Semantic Technologies ◽

Sources Of Information ◽

Accurate Analysis ◽

Online Social Media ◽

Semantic Orientation ◽

Semantically Enhanced

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.

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Experimental investigation of automated system for twitter sentiment analysis to predict the public emotions using machine learning algorithms

Materials Today Proceedings ◽

10.1016/j.matpr.2020.09.351 ◽

2020 ◽

Author(s):

Priti Sharma ◽

A.K. Sharma

Keyword(s):

Machine Learning ◽

Experimental Investigation ◽

Sentiment Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Automated System ◽

The Public

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Ai4Truth: An In-depth Analysis on Misinformation using Machine Learning and Data Science

10.5121/csit.2021.112327 ◽

2021 ◽

Author(s):

Kevin Qu ◽

Yu Sun

Keyword(s):

Machine Learning ◽

Data Science ◽

Social Issues ◽

Fake News ◽

Future Studies ◽

The Public ◽

Applied Machine Learning ◽

Depth Analysis ◽

Real Challenge ◽

The Way

A number of social issues have been grown due to the increasing amount of “fake news”. With the inevitable exposure to this misinformation, it has become a real challenge for the public to process the correct truth and knowledge with accuracy. In this paper, we have applied machine learning to investigate the correlations between the information and the way people treat it. With enough data, we are able to safely and accurately predict which groups are most vulnerable to misinformation. In addition, we realized that the structure of the survey itself could help with future studies, and the method by which the news articles are presented, and the news articles itself also contributes to the result.

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A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

Journal of Informatics Electrical and Electronics Engineering (JIEEE) ◽

10.54060/jieee/002.02.009 ◽

2021 ◽

Vol 2 (2) ◽

pp. 1-10

Author(s):

Amrita Mishra ◽

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Text Classification ◽

Comprehensive Analysis ◽

Social Media Data ◽

The Public ◽

Opinion Analysis ◽

Twitter Data ◽

Media Data

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.

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Sentiment Analysis on Twitter Hashtag Datasets

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39201 ◽

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

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Applied Machine Learning Techniques on Selection and Positioning of Human Resources in the Public Sector

Open Journal of Business and Management ◽

10.4236/ojbm.2021.92030 ◽

2021 ◽

Vol 09 (02) ◽

pp. 536-556

Author(s):

Panagiota Pampouktsi ◽

Spyridon Avdimiotis ◽

Manolis Μaragoudakis ◽

Markos Avlonitis

Keyword(s):

Machine Learning ◽

Public Sector ◽

Human Resources ◽

Machine Learning Techniques ◽

The Public ◽

Applied Machine Learning ◽

Learning Techniques

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Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM)

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.0813944 ◽

2021 ◽

Vol 8 (1) ◽

pp. 147

Author(s):

Primandani Arsi ◽

Retno Waluyo

Keyword(s):

Machine Learning ◽

Social Media ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Public Discourse ◽

Capital City ◽

Support Vector ◽

The Public ◽

Machine Learning Methods ◽

Classification Technique

Dewasa ini, media sosial berkembang pesat di internet, salah satu yang banyak digemari adalah Twitter. Berbagai topik ramai diperbincangkan di Twitter mulai dari ekonomi, politik, sosial, budaya, hukum dan lain-lain. Salah satu topik yang ramai diperbincangkan di Twitter adalah terkait isu pemindahan ibu kota Indonesia. Namun dibalik hal tersebut terdapat kontroversi dari pihak yang merasa pro dan kontra, masing-masing memiiki sudut pandang yang berbeda. Hal ini menyebabkan munculnya fenomena perdebatan khususnya di Twitter yang sebenarnya menunjukkan perhatian kolektif mengenai wacana publik tersebut. Analisis sentimen adalah proses mengekstraksi, memahami dan mengolah data berupa teks yang tidak terstruktur secara otomatis guna mendapatkan informasi sentimen yang terdapat pada sebuah kalimat pendapat atau opini. Dalam penerapan analisis sentimen menggunakan metode machine learning terdapat beberapa metode yang sering digunakan. Dalam penelitian ini diusulkan metode Support Vector Machine (SVM) untuk diterapkan pada tweets topik pemindahan ibu kota Indonesia untuk tujuan klasifikasi kelas sentimen pada media sosial twitter. Teknis klasifikasi dilakukan dengan cara mengklasifikasikan menjadi 2 kelas yakni positif dan negatif. Berdasarkan hasil pengujian yang dilakukan terhadap tweets sentimen pemindahan ibu kota dari media sosial twitter sebanyak 1.236 tweets (404 positif dan 832 negatif) menggunakan SVM diperoleh akurasi =96,68%, precision=95.82%, recall=94.04% dan AUC = 0,979. AbstractToday, social media is growing fast on the internet.One of the most popular social media is Twitter. Many topics are discussed on Twitter such as economic, politic, social, culture, and law. One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However, there is controversy from supporters and opponents. They have different views. This issue leads to a phenomenon of debate on Twitter that actually shows a collective concern about the public discourse. Sentiment analysis is a process of extracting, understanding and processing unstructured data to get sentiment information which is found in an opinion sentence. Application of sentiment analysis using machine learning methods shows that there are several methods that are often used. In this study, the Support Vector Machine (SVM) method is proposed to be applied to tweets on the topic of relocating Indonesia's capital city for sentiment classification on social media twitter. The classification technique is carried out into 2 classes, namely positive and negative. Based on testing on the sentiment of relocating Indonesia's capital city from social media twitter from 1,116 tweets (404 positive and 832 negative) using SVM obtained accuracy = 96.68%, precision = 95.82%, recall = 94.04% and AUC = 0.979.

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