Twitter Sentiments Analysis Using Machine Learning

2020 ◽  
pp. 312-320
Author(s):  
Saurabh Singh
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Maximum Length ◽  
Product Reviews ◽  
The People

Twitter sentiment analysis is the method of Natural Language Processing (NLP). In this project named Twitter sentiment Analysis we analyze the sentiments behind the twitter’s tweet. We have three type of sentiment: Positive, Neutral and Negative. Analyzing the sentiments behind every tweet is the biggest problem in the early days but now it can be solved with the help of Machine Learning. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters and through the Twitter Sentimental Analysis we can analysis the mood of the person who tweet which can helps in the industries to analyze the market and their product reviews or we can know the sentiments behind the opinion on any topic on which the group of people tweet and through this we can find the final result that the people point on view on the particular topic, product and any other tweets suggestions.

scholarly journals Sentiment Analysis of Movie Review using Machine Learning Approach

IJOSTHE ◽  
2017 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Rajul Rai ◽  
Pradeep Mewada
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
The People ◽  
Textual Data ◽  
Machine Learning Approach ◽  
Regional Languages

With development of Internet and Natural Language processing, use of regional languages is also grown for communication. Sentiment analysis is natural language processing task that extracts useful information from various data forms such as reviews and categorize them on basis of polarity. One of the sub-domain of opinion mining is sentiment analysis which is basically focused on the extraction of emotions and opinions of the people towards a particular topic from textual data. In this paper, sentiment analysis is performed on IMDB movie review database. We examine the sentiment expression to classify the polarity of the movie review on a scale of negative to positive and perform feature extraction and ranking and use these features to train our multilevel classifier to classify the movie review into its correct label. In this paper classification of movie reviews into positive and negative classes with the help of machine learning. Proposed approach using classification techniques has the best accuracy of about 99%.

scholarly journals Sentiment Analysis on Twitter Airline Data

2021 ◽  
Vol 9 (VI) ◽  
pp. 3767-3770
Author(s):  
Kirti Jain
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Task ◽  
Model Based ◽  
Sentiment Mining ◽  
General Opinion

Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.

scholarly journals Generalized approach to sentiment analysis of short text messages in natural language processing

2020 ◽  
pp. 2-14
Author(s):  
Evrenii Polyakov ◽  
Leonid Voskov ◽  
Pavel Abramov ◽  
Sergey Polyakov
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Full Range ◽  
Text Messages ◽  
Basic Solution ◽  
Short Text

Introduction: Sentiment analysis is a complex problem whose solution essentially depends on the context, field of study andamount of text data. Analysis of publications shows that the authors often do not use the full range of possible data transformationsand their combinations. Only a part of the transformations is used, limiting the ways to develop high-quality classification models.Purpose: Developing and exploring a generalized approach to building a model, which consists in sequentially passing throughthe stages of exploratory data analysis, obtaining a basic solution, vectorization, preprocessing, hyperparameter optimization, andmodeling. Results: Comparative experiments conducted using a generalized approach for classical machine learning and deeplearning algorithms in order to solve the problem of sentiment analysis of short text messages in natural language processinghave demonstrated that the classification quality grows from one stage to another. For classical algorithms, such an increasein quality was insignificant, but for deep learning, it was 8% on average at each stage. Additional studies have shown that theuse of automatic machine learning which uses classical classification algorithms is comparable in quality to manual modeldevelopment; however, it takes much longer. The use of transfer learning has a small but positive effect on the classificationquality. Practical relevance: The proposed sequential approach can significantly improve the quality of models under developmentin natural language processing problems.

Deep Learning Approaches for Textual Sentiment Analysis

2020 ◽  
pp. 171-182 ◽  
Author(s):  
Tamanna Sharma ◽  
Anu Bajaj ◽  
Om Prakash Sangwan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Computational Technique ◽  
Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

scholarly journals Sentiment Analysis of Review Data of a Product Using Python

2018 ◽  
Vol 7 (3.12) ◽  
pp. 674
Author(s):  
P Santhi Priya ◽  
T Venkateswara Rao
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
The Other ◽  
Product Reviews ◽  
Prime Problem

The other name of sentiment analysis is the opinion mining. It’s one of the primary objectives in a Natural Language Processing(NLP). Opinion mining is having a lot of audience lately. In our research we have taken up a prime problem of opinion mining which is theSentiment Polarity Categorization(SPC) that is very influential. We proposed a methodology for the SPC with explanations to the minute level. Apart from theories computations are made on both review standard and sentence standard categorization with benefitting outcomes. Also, the data that is represented here is from the product reviews given on the shopping site called Amazon.  

scholarly journals Sentiment Analysis Using Machine Learning Approaches (Lexicon based on movie review dataset)

2020 ◽  
Vol 2 (3) ◽  
pp. 145-152
Author(s):  
Ayushi Mitra
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Research Work ◽  
Emotional States ◽  
Learning Approaches ◽  
Data Set

Sentiment analysis or Opinion Mining or Emotion Artificial Intelligence is an on-going field which refers to the use of Natural Language Processing, analysis of text and is utilized to extract quantify and is used to study the emotional states from a given piece of information or text data set. It is an area that continues to be currently in progress in field of text mining. Sentiment analysis is utilized in many corporations for review of products, comments from social media and from a small amount of it is utilized to check whether or not the text is positive, negative or neutral. Throughout this research work we wish to adopt rule- based approaches which defines a set of rules and inputs like Classic Natural Language Processing techniques, stemming, tokenization, a region of speech tagging and parsing of machine learning for sentiment analysis which is going to be implemented by most advanced python language.

scholarly journals Multilingual Lexicon based Approach for Real-Time Sentiment Analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 984-989
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Political Parties ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Common People ◽  
Svm Algorithm

The information on WWW has mounted to a greater height, overriding to fledgling analysis in the direction of sentiments using Artificial Intelligence. Sentiment Analysis deals with the calculus exploration of sentiments, opinions and subjectivity. In this paper, multilingual tweets are analyzed for identifying the polarities of various political parties like AAP, BJP, Samajwadi, BSP and Congress; so that the users will get an idea that to which party they should give their vote. The data is being analyzed using Natural Language Processing. Using different smoothening techniques, noise is removed from data, classified by using Machine learning algorithms and then the accuracy of the system is gauged using various evaluation precision measures. The central premise of this research is to benignant common people and politicians both. For common people; is for deciding their precious vote, to which party to give will be good for themselves and nation too. For politicians; they will have an idea about themselves i.e. after seeking the polarities of different parties, the politicians will have an idea which party is preferable and which is not preferable, so that the politicians can work accordingly. The system shows comparison among VADER and SVM algorithm; and SVM algorithm showed 90% accuracy.

Tasks, Approaches, and Avenues of Opinion Mining, Sentiment Analysis, and Emotion Analysis

2021 ◽  
pp. 171-209
Author(s):  
Amira M. Idrees ◽  
Fatma Gamal Eldin ◽  
Amr Mansour Mohsen ◽  
Hesham Ahmed Hassan
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Potential Benefit ◽  
Opinion Mining ◽  
Product Reviews ◽  
Huge Amount ◽  
Know How

Every successful business aims to know how customers feel about its brands, services, and products. People freely express their views, ideas, sentiments, and opinions on social media for their day-to-day activities, for product reviews, for surveys, and even for their public opinions. This process provides a fortune of valuable resources about the market for any type of business. Unfortunately, it's impossible to manually analyze this massive quantity of information. Sentiment analysis (SA) and opinion mining (OM), as new fields of natural language processing, have the potential benefit of analyzing such a huge amount of data. SA or OM is the computational treatment of opinions, sentiments, and subjectivity of text. This chapter introduces the reader to a survey of different text SA and OM proposed techniques and approaches. The authors discuss in detail various approaches to perform a computational treatment for sentiments and opinions with their strengths and drawbacks.

scholarly journals Movie Review System using Sentiment Analysis and Social Networking Platforms (SNPs)

2021 ◽  
Vol 9 (VI) ◽  
pp. 1492-1499
Author(s):  
Ms. Pooja Dhiwar S.
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Social Networking ◽  
Digital Media ◽  
Language Processing ◽  
Naive Bayes ◽  
Recommendation Systems ◽  
Review System ◽  
Movie Recommendation

Today online social networking platforms SNPs have become an integral part of or life where we share a lot of information of all the things, we do in life from shopping to movie watching. With ever growing use of SNPs recommendation systems have emerged as a hot trend for applications in e-commerce and digital media. These recommendation systems are useful as well as misguiding. Today digital media use has increased tremendously with increase in internet speeds. But users do not get proper review of a movie and a user is lured to watch a substandard movie which he does not intended to do, thus costing a user time and money. So, there is a need of developing a movie review which will give correct reviews of a digital content like movies so he can only movies which he intends to do. So, we are studying various techniques authored by various authors and create a good movie review system of our own. The first technique we studied and intends to use is movie recommendation system using tweets. The second study is movie recommendation using similarity measures. The third study does find a public shamming using SNP. These techniques are useful and we propose to use some part of each in our new movie review framework by improving the techniques drawbacks. The new framework will be a combination of data from more than one SNP and using natural language processing and machine learning on the data. We are going to use two machine learning algorithms SVM and Naïve Bayes for this purpose. For natural language processing of SNP data, we are going to use OPEN-NLP. We intend to use SNPs such as Twitter and any other movie database like IMDB etc. for data on the movie. The movie will be classified in three classes bad, good and excellent. The results from each algorithm SVM and Naïve bayes will be analyzed for each SNP and try to give user a more accurate movie review by combining all the reviews together and classes accuracy and show overall prediction results with a rating. To get more accurate results for each movie we are going to create a dataset for each movie for demonstration and will not depend on a single combined dataset as keywords for each movie may be different. We are going to combine datasets for each movie from multiple SNPs.

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