Sentiment Analysis of Movie Review using Machine  Learning Techniques

Today's online world was fully filled up with blogs, views, comments, posts through various websites and social-surfs. People were habituated with posting every incident into blogs, messed with comments like text and emotions, which are a mixed bag of sad, happy, worry, cry etc. Analysing such data was called as Sentimental Analysis. To analysis, these unordered data we use new emerged technology algorithms. Machine learning a transpire technology which is engaged with almost all the fields, where its algorithms are more powerful that give with better faultless results. In this paper, we are analyzing tweets based on movie reviews using the Multinomial Logistic Regression, Naïve Bayes, and SVM algorithms to compare score value to show the best text analysis algorithm.

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Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Journal of the Nigerian Society of Physical Sciences ◽

10.46481/jnsps.2021.308 ◽

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.

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Twitter Sentiment Analysis using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c6281.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 4205-4209

Keyword(s):

Logistic Regression ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Machine Learning Techniques ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Learning Techniques ◽

Social Media Platforms

Nowadays people share their views and opinions in twitter and other social media platforms, the way of recognizing sentiments and speculation in tweets is Twitter Sentiment Analysis. Determining the contradiction or sentiment of the tweets and then listing them into positive, negative and neutral tweets is the main classifying step in this process. The issue related to sentiment analysis is the naming of the correct congruous sentiment classifier algorithm to list the tweets. The foundation classifier techniques like Logistic regression, Naive Bayes classifier, Random Forest and SVMs are normally used. In this paper, the Naïve Bayes classifier and Logistic Regression has been used to perform sentiment analysis and classify based on the better accuracy of catagorizing Technique. The outcome shows that Naive Bayes classifier works better for this approach. Data pre-processing and feature extraction is realized as a portion of task.

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Sentiment Analysis of Tweets on the COVID-19 Pandemic Using Machine Learning Techniques

Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-6870-5.ch021 ◽

2021 ◽

pp. 310-320

Author(s):

Jothikumar R. ◽

Vijay Anand R. ◽

Visu P. ◽

Kumar R. ◽

Susi S. ◽

...

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Respiratory Tract ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Machine Learning Techniques ◽

Respiratory Tract Diseases ◽

Thought Processes ◽

Learning Techniques

Sentiment evaluation alludes to separate the sentiments from the characteristic language and to perceive the mentality about the exact theme. Novel corona infection, a harmful malady ailment, is spreading out of the blue through the quarter, which thought processes respiratory tract diseases that can change from gentle to extraordinary levels. Because of its quick nature of spreading and no conceived cure, it ushered in a vibe of stress and pressure. In this chapter, a framework perusing principally based procedure is utilized to discover the musings of the tweets related to COVID and its effect lockdown. The chapter examines the tweets identified with the hash tags of crown infection and lockdown. The tweets were marked fabulous, negative, or fair, and a posting of classifiers has been utilized to investigate the precision and execution. The classifiers utilized have been under the four models which incorporate decision tree, regression, helpful asset vector framework, and naïve Bayes forms.

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Risk Factor Prediction by Naive Bayes Classifier, Logistic Regression Models, Various Classification and Regression Machine Learning Techniques

Proceedings of the National Academy of Sciences India Section B Biological Sciences ◽

10.1007/s40011-021-01278-3 ◽

2021 ◽

Author(s):

K. Kannan ◽

A. Menaga

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Risk Factor ◽

Regression Models ◽

Naive Bayes ◽

Machine Learning Techniques ◽

Bayes Classifier ◽

Logistic Regression Models ◽

Learning Techniques ◽

Classification And Regression

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Classification of sentence level sentiment analysis using cloud machine learning techniques

Cluster Computing ◽

10.1007/s10586-017-1200-1 ◽

2017 ◽

Vol 22 (S1) ◽

pp. 1199-1209 ◽

Cited By ~ 26

Author(s):

R. Arulmurugan ◽

K. R. Sabarmathi ◽

H. Anandakumar

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Sentence Level

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

Digital Scholarship in the Humanities ◽

10.1093/llc/fqy037 ◽

2018 ◽

Vol 34 (3) ◽

pp. 569-581 ◽

Cited By ~ 1

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.

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A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.32.16268 ◽

2018 ◽

Vol 7 (2.32) ◽

pp. 462

Author(s):

G Krishna Chaitanya ◽

Dinesh Reddy Meka ◽

Vakalapudi Surya Vamsi ◽

M V S Ravi Karthik

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Language Processing ◽

Machine Learning Techniques ◽

Buying Behavior ◽

Online Retail ◽

User Reviews ◽

Business Plans ◽

Learning Techniques ◽

User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general.

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