Twitter Sentiment Analysis Using Python and Machine Learning Algorithm

Introduction

Microblogging sites like Twitter have become crucial sources of diverse information, reflecting public opinions on various topics, discussing current issues, and sharing product feedback. Sentiment analysis is the process of extracting qualitative information from text, essentially turning unstructured data into structured insights. This paper focuses on sentiment analysis applied to Twitter data, aiming to understand user sentiments through machine learning classifiers and Python programming.

Abstract

Twitter feeling examination is a use of notion investigation on information from Twitter (tweets), to separate client's suppositions and sentiments.

The principle objective is to investigate how message investigation procedures can be utilized to delve into a portion of the information in a progression of posts concentrating on various patterns of tweets dialects, tweets volumes on z twitter. Exploratory assessments demonstrate that the proposed AI classifiers are proficient and perform better as far as exactness and time.

The proposed calculation is executed in python. Online networking contains an enormous measure of the assumption information as tweets, sites, and updates on the status, posts, and so on.

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In this paper, the most prominent miniaturized scale blogging stage Twitter is utilized The main installation software‘s include tweepy, text blob, nltk .Through the various python packages and api we can extract the tweet data from twitter for analyzing the tweets .After collecting all those data's(tweets)are stored into open source database through connection api in scripts

Preprocessing Data

Data collected from Twitter undergoes preprocessing in Python, involving steps like tokenization, removal of hashtags, numbers, URLs, emoticons replacement, and stop words elimination.

This stage prepares the data for further analysis.

Feature Extraction and Selection

Feature extraction involves selecting useful words from tweets, employing techniques like unigram, bigram, and n-gram features, and using Parts Of Speech Tags to indicate subjectivity and sentiment. Feature selection enhances sentiment analysis accuracy through methods like natural language processing, statistical approaches, clustering-based, and hybrid techniques.

Feature selection

Correct feature selection techniques are used in sentiment analysis that has got a huge job for recognizing pertinent properties and expanding arrangement (AI) precision. They are sorted into 4 primary sorts to be specific

1. Common language preparing
2. Factual
3. Bunching based
4. Cross breed

Normal language preparing mostly chips away at (1) Noun, thing phrases, descriptive words, adverbs.(2)Terms happening close emotional articulations can go about as highlight Statistical methods are additionally separated into three sub types.

• Univariater
• Multivariate
• Hybrid.

Clustering based component extraction procedures are Implemented by requiring couple of parameters. The real shortcoming of bunching is that lone significant highlights can be removed and it is hard to separate minor.

Machine Learning Algorithms

AI is the investigation of calculations that can gain from and make forecasts on information. It is additionally called as identified with expectation making on certain information. There are many AI calculations. Be that as it may, this paper clarifies around two of them. They are:

• Naïve baye's
• Neural systems

Naive Bayes classifiers work by assuming the independence of features within a class. It's particularly effective for large datasets. The posterior probability, used for classification, is calculated using the formula:

p(c∣x)=p(x)p(x∣c)p(c)

where p(c∣x) is the posterior probability of class c given predictor x, p(c) is the prior probability of class, p(x∣c) is the likelihood, and p(x) is the prior probability of the predictor.

Neural Network

A neural network is another important tool for classification. Is has also been a promising alternative to various classification methods. This classifier with the appropriate network structure can handle the correlation or dependence between the input variables. An artificial neural network performs back propagation by activating the neurons in the hidden layer.

Conclusion and Future Scope

Sentiment analysis on Twitter data provides valuable insights for decision-making processes, evolving rapidly as a field. This project aims to analyze sentiments of various topics from Twitter, achieving higher accuracy in sentiment classification (positive, negative, neutral) compared to existing systems. Future work could enhance sentiment analysis models with more semantic and syntactic improvements, incorporating multiple languages for comprehensive analysis and representation.

A lot of research is present in literature for recognizing estimation from the content. In any case, there is an immense extent of progress of these current assumption investigation models. Existing assumption examination models can be improve further with more sematic and conventional. We are principally made arrangements for future extension is united all the language for the specific points then examination at long last speak to in the specific yield position alongside the individual language that will improve the more exactness .diminished the time multifaceted nature no compelling reason to preparing for the individual language.