What You Need to Know About Social Media Sentiment Analysis
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Explore practical tips and techniques for easy sentiment analysis on social media, presented by Josie Broadstock.
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Sentiment analysis using ml
This document discusses using machine learning for sentiment analysis on Twitter data. It defines machine learning and different types of machine learning like supervised and unsupervised learning. It then defines sentiment analysis as identifying subjective information from text and classifying it as positive, negative, or neutral. The document outlines the process of collecting Twitter data, preprocessing it, analyzing sentiment using algorithms like Naive Bayes and decision trees, and presenting the results. It acknowledges challenges like informal language and discusses how the proposed system could provide useful insights for businesses.
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Sentiment analysis, also referred to as opinion mining, is a method to identify and assess sentiments expressed within a text. The primary purpose is to gauge whether the attitude towards a specific topic, product, or service is positive, negative, or neutral. This process utilizes AI and natural language processing (NLP) to interpret human language and its intricacies, allowing machines to understand and respond to our emotions.
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This document outlines a project on text extraction and sentiment analysis from social media. It discusses extracting tweets using APIs, preprocessing the text by removing stop words and noise, extracting features like capitalization and emojis, and classifying the sentiment using algorithms like Naive Bayes. The goal is to build a tool that can measure sentiment polarity accurately. It describes the modules including data collection, tokenization, preprocessing, feature extraction, and classification. Future work includes improving the dictionary and parameters to enhance accuracy and developing mobile applications.
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Sentiment analysis using ml
This document discusses using machine learning for sentiment analysis on Twitter data. It defines machine learning and different types of machine learning like supervised and unsupervised learning. It then defines sentiment analysis as identifying subjective information from text and classifying it as positive, negative, or neutral. The document outlines the process of collecting Twitter data, preprocessing it, analyzing sentiment using algorithms like Naive Bayes and decision trees, and presenting the results. It acknowledges challenges like informal language and discusses how the proposed system could provide useful insights for businesses.
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Six month major project on text classification with twitter sentiment analysis of US airlines.
It tells the importance of data and reviews given by the users for different airlines and helps recommending options to improve user experience.
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Social media sentiment analysis is a natural language processing (NLP) technique used for understanding the emotions behind user-generated content from social media mining. It gives a clear sense of how people feel about your brand.
AI for sentiment analysis - An Overview.pdf
Sentiment analysis, also referred to as opinion mining, is a method to identify and assess sentiments expressed within a text. The primary purpose is to gauge whether the attitude towards a specific topic, product, or service is positive, negative, or neutral. This process utilizes AI and natural language processing (NLP) to interpret human language and its intricacies, allowing machines to understand and respond to our emotions.
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This document outlines a project on text extraction and sentiment analysis from social media. It discusses extracting tweets using APIs, preprocessing the text by removing stop words and noise, extracting features like capitalization and emojis, and classifying the sentiment using algorithms like Naive Bayes. The goal is to build a tool that can measure sentiment polarity accurately. It describes the modules including data collection, tokenization, preprocessing, feature extraction, and classification. Future work includes improving the dictionary and parameters to enhance accuracy and developing mobile applications.
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This document presents a live Twitter sentiment analysis application that performs sentiment analysis on tweets in real-time about a topic entered by the user. It uses the Twitter API to stream tweets, the VADER sentiment analysis library to analyze sentiment, and pyLDAvis for topic modeling. The application cleans tweets by removing emojis, stopwords, punctuations, and lemmatizing words. It then uses VADER to classify sentiment and displays results interactively. PyLDAvis performs topic modeling on tweets to discover topics and display keywords. The application allows users to explore emotions and themes in Twitter conversations about their interests of interest in a dynamic, interactive manner.
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The document provides an overview of sentiment analysis and summarizes the current approaches used. It discusses how machine learning classifiers like Naive Bayes can be used for sentiment classification of texts, treating it as a two-class text classification problem. It also mentions the use of natural language processing techniques. The current system discussed will use machine learning and NLP for sentiment analysis of tweets, training classifiers on labeled tweet data to classify the polarity of new tweets.
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1) Data analytics is the process of examining large data sets to uncover patterns and insights. It involves descriptive, predictive, and prescriptive analysis.
2) Descriptive analysis summarizes past events, predictive analysis forecasts future events, and prescriptive analysis recommends actions.
3) Major companies like Facebook, Amazon, Uber, banks and Spotify extensively use big data and data analytics to improve customer experience, detect fraud, personalize recommendations and gain business insights.
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● The growing importance of sentiment analysis coincides with the popularity of social network platform (Facebook, Twitter, Flickr).
● A tremendous amount of data in different forms including text, image, and videos makes sentiment analysis a very challenging task.
● In this chapter, we will discuss some of the latest works on topics of sentiment analysis based on visual content and textual content.
IRJET - Sentiment Analysis for Marketing and Product Review using a Hybrid Ap...
This document presents a hybrid approach for sentiment analysis that combines a lexicon-based technique and a machine learning technique using recurrent neural networks. It aims to analyze sentiments expressed in tweets towards products and services more accurately. The proposed model first cleans tweets collected from Twitter APIs. It then classifies the tweets' sentiment using both a lexicon-based technique using TextBlob and an LSTM-RNN model. The hybrid approach provides not only classification of sentiment but also a score of sentiment strength. This combined approach seeks to gain deeper insights than single techniques alone.
Emotion analysis
BytesView's advanced machine learning techniques can help you analyze the emotions expressed by the author in a piece of text.
It can be easily done based on the types of feelings expressed in the text such as fear, anger, happiness, sadness, love, inspiring, or neutral.
IRJET- Sentimental Analysis of Twitter for Stock Market Investment
This document discusses using sentiment analysis on Twitter data to predict stock market movements. It proposes collecting tweets related to various companies, analyzing the sentiment polarity of the tweets, extracting features using n-grams, and training machine learning models like logistic regression and decision trees to predict stock price changes. The models would be trained on the sentiment scores of tweets along with market data to analyze correlations and enable stock price predictions. The goal is to see if analyzing public sentiment on Twitter can help predict company stock performance and guide investment decisions.
A review on sentiment analysis and emotion detection.pptx
This document provides an overview of sentiment analysis and emotion detection from text. It discusses how social media generates massive amounts of textual data that can be analyzed using these techniques. The document outlines several key topics:
- The levels of sentiment analysis including sentence, document and aspect levels.
- Popular emotion models like dimensional and categorical models.
- The basic steps involved in sentiment/emotion detection including preprocessing, feature extraction, and classification.
- Challenges in the field like dealing with context, slang, and ambiguity.
It provides examples of techniques like lexicon-based, machine learning-based and deep learning-based approaches.
Major presentation
The document describes a research project on sentiment analysis of tweets. It involves collecting twitter data, preprocessing the data by removing stopwords and replacing emoticons/sentiment words with tags. Features are then extracted and normalized, followed by feature reduction. The data is clustered into positive and negative classes using K-means clustering and Differential Evolution algorithm, and their accuracies are compared, with Differential Evolution found to perform better. Future work proposed includes applying additional clustering techniques and comparing with supervised learning methods.
IRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: Twisent
The document describes a real-time Twitter sentiment analysis and visualization system called TwiSent. TwiSent uses a lexicon-based approach for sentiment analysis on tweets collected in real-time from Twitter using hashtags and keywords. It analyzes the sentiment of tweets as positive or negative and visualizes the results in a web application. The system aims to help organizations, political parties, and individuals better understand opinions on social media and make improved decisions.
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Sentiment analysis using machine learning and deep Learning
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With the increasing rate at which data is created by internet users on various platforms, it becomes necessary to analyze and make use of the data by the Defense and other Government Organizations and know the sentiment of the people. This shall help the organizations take control of their actions and decide the steps to be taken shortly. Added to it, when something crucial is happening in the nation, it is of paramount importance to decide every step without hurting/violating the sentiments of the people. In the era of Microblogging, which has become quite a popular tool of communication, millions of users share their views and opinions on various day-to-day life issues concerning them directly or indirectly through social media platforms like Twitter, Reddit, Tumblr, Facebook. Data from these sites can be efficiently used for marketing or social studies. In this paper, we have taken into account various methods to perform sentiment analysis. Sentiment Analysis has been performed by using Machine Learning Classifiers. Polarity-based sentiment analysis, and Deep Learning Models are used to classify user's tweets as having `positive' or `negative' sentiment. The idea behind taking in various model architectures was to account for the variance in the opinions and thoughts existing on such social media platforms. These classification models can further be implemented to classify live tweets on twitter on any topic
Combining Lexicon based and Machine Learning based Methods for Twitter Sentim...
This document discusses combining lexicon-based and machine learning methods for Twitter sentiment analysis. It first describes lexicon-based approaches like TextBlob and Vader that use sentiment lexicons to determine tweet polarity. It then discusses machine learning approaches like random forest, support vector machines, and decision trees that are trained on labeled tweet data. The document finds that a random forest classifier achieved the highest accuracy of 99.92% at predicting tweet sentiment, demonstrating the effectiveness of combining both lexicon-based and machine learning methods for Twitter sentiment analysis.
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Building a Sentiment Analytics Solution Powered by Machine Learning- Impetus ...
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This document discusses building a sentiment analysis solution powered by machine learning. It begins with an introduction to sentiment analysis and outlines the existing landscape of solutions. It then discusses challenges like accuracy and isolating content types. The document proposes that machine learning can help address these challenges by analyzing sentiment versus subjectivity, polarity reactions, and sentiment intensity. It describes how to build such a solution using machine learning, including creating a knowledge base and leveraging machine learning algorithms. Finally, it outlines Impetus Technologies' sentiment analysis solution and the benefits it provides.what is sentiment analysis.pptx
This PowerPoint presentation provides a concise introduction to sentiment analysis, exploring its role in analyzing emotions and opinions in text data. It covers the process of sentiment analysis, applications in customer experience, brand reputation, market research, and political analysis. The presentation highlights the benefits of sentiment analysis while acknowledging challenges in understanding context and ensuring data quality. It serves as a valuable resource for understanding the power of sentiment analysis in extracting insights from textual content.
SA
The document describes a sentiment analysis tool called STARK that analyzes user reviews of products. STARK can summarize what users talked about in a product, analyze sentiment towards specific features like cameras, show overall sentiment distributions, identify the most discussed features, and quantify sentiment scores for each feature. It addresses challenges in understanding context and uses natural language processing, sentiment scoring algorithms, and visualizations to help companies understand user feedback.
Political Prediction Analysis using text mining and deep learning.pptx
Social media platforms have vast users connected platforms like Twitter has 330 Million,
Facebook has 2.2 Billion, Google+ has 111 Million and LinkedIn has 467 Million which is
enough to create an impression through social media. We have proposed a system to
determine current sentiment on twitter using Twitter API for open access which includes
opinions from different content structures like latest news, audits, articles and social media
posts. And Deep Learning method to study Historic Data for predicting future results.
Previous implementations of prediction analysis on Twitter Data were not successful to fulfill
analysis on live Twitter Data. Thus, we have proposed a system which will predict the result
on live Twitter data and also generate the statistical graph which classify the polarity of
positive and negative tweets. With the help graphs, reports, trends and tweets one can predict
the future results of the political party and also can be used to create the campaigns.
A Review of machine learning approaches to mine Social Choice of voters.
This document presents a literature review of machine learning approaches used to mine social media data and predict voter choice or election results. It discusses how natural language processing techniques like tokenization and vectorization are used to structure social media data. Lexicon-based models and supervised learning algorithms like Naive Bayes, maximum entropy, logistic regression, and support vector machines are then used to analyze sentiment and predict if a user expresses positive, negative or neutral intent towards a candidate or policy. Deep learning models like convolutional and recurrent neural networks also show promise by identifying patterns in large unlabeled datasets. While current approaches predict elections accurately, the document suggests ensemble and deep learning models that can process more data could improve predictions of how voters' social media expressions correspond to their
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Live Twitter Sentiment Analysis and Interactive Visualizations with PyLDAvis ...
This document presents a live Twitter sentiment analysis application that performs sentiment analysis on tweets in real-time about a topic entered by the user. It uses the Twitter API to stream tweets, the VADER sentiment analysis library to analyze sentiment, and pyLDAvis for topic modeling. The application cleans tweets by removing emojis, stopwords, punctuations, and lemmatizing words. It then uses VADER to classify sentiment and displays results interactively. PyLDAvis performs topic modeling on tweets to discover topics and display keywords. The application allows users to explore emotions and themes in Twitter conversations about their interests of interest in a dynamic, interactive manner.
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This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
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The document provides an overview of sentiment analysis and summarizes the current approaches used. It discusses how machine learning classifiers like Naive Bayes can be used for sentiment classification of texts, treating it as a two-class text classification problem. It also mentions the use of natural language processing techniques. The current system discussed will use machine learning and NLP for sentiment analysis of tweets, training classifiers on labeled tweet data to classify the polarity of new tweets.
Data analytics
1) Data analytics is the process of examining large data sets to uncover patterns and insights. It involves descriptive, predictive, and prescriptive analysis.
2) Descriptive analysis summarizes past events, predictive analysis forecasts future events, and prescriptive analysis recommends actions.
3) Major companies like Facebook, Amazon, Uber, banks and Spotify extensively use big data and data analytics to improve customer experience, detect fraud, personalize recommendations and gain business insights.
Multimedia data minig and analytics sentiment analysis using social multimedia
● The growing importance of sentiment analysis coincides with the popularity of social network platform (Facebook, Twitter, Flickr).
● A tremendous amount of data in different forms including text, image, and videos makes sentiment analysis a very challenging task.
● In this chapter, we will discuss some of the latest works on topics of sentiment analysis based on visual content and textual content.
IRJET - Sentiment Analysis for Marketing and Product Review using a Hybrid Ap...
This document presents a hybrid approach for sentiment analysis that combines a lexicon-based technique and a machine learning technique using recurrent neural networks. It aims to analyze sentiments expressed in tweets towards products and services more accurately. The proposed model first cleans tweets collected from Twitter APIs. It then classifies the tweets' sentiment using both a lexicon-based technique using TextBlob and an LSTM-RNN model. The hybrid approach provides not only classification of sentiment but also a score of sentiment strength. This combined approach seeks to gain deeper insights than single techniques alone.
Emotion analysis
BytesView's advanced machine learning techniques can help you analyze the emotions expressed by the author in a piece of text.
It can be easily done based on the types of feelings expressed in the text such as fear, anger, happiness, sadness, love, inspiring, or neutral.
IRJET- Sentimental Analysis of Twitter for Stock Market Investment
This document discusses using sentiment analysis on Twitter data to predict stock market movements. It proposes collecting tweets related to various companies, analyzing the sentiment polarity of the tweets, extracting features using n-grams, and training machine learning models like logistic regression and decision trees to predict stock price changes. The models would be trained on the sentiment scores of tweets along with market data to analyze correlations and enable stock price predictions. The goal is to see if analyzing public sentiment on Twitter can help predict company stock performance and guide investment decisions.
A review on sentiment analysis and emotion detection.pptx
This document provides an overview of sentiment analysis and emotion detection from text. It discusses how social media generates massive amounts of textual data that can be analyzed using these techniques. The document outlines several key topics:
- The levels of sentiment analysis including sentence, document and aspect levels.
- Popular emotion models like dimensional and categorical models.
- The basic steps involved in sentiment/emotion detection including preprocessing, feature extraction, and classification.
- Challenges in the field like dealing with context, slang, and ambiguity.
It provides examples of techniques like lexicon-based, machine learning-based and deep learning-based approaches.
Major presentation
The document describes a research project on sentiment analysis of tweets. It involves collecting twitter data, preprocessing the data by removing stopwords and replacing emoticons/sentiment words with tags. Features are then extracted and normalized, followed by feature reduction. The data is clustered into positive and negative classes using K-means clustering and Differential Evolution algorithm, and their accuracies are compared, with Differential Evolution found to perform better. Future work proposed includes applying additional clustering techniques and comparing with supervised learning methods.
IRJET- A Real-Time Twitter Sentiment Analysis and Visualization System: Twisent
The document describes a real-time Twitter sentiment analysis and visualization system called TwiSent. TwiSent uses a lexicon-based approach for sentiment analysis on tweets collected in real-time from Twitter using hashtags and keywords. It analyzes the sentiment of tweets as positive or negative and visualizes the results in a web application. The system aims to help organizations, political parties, and individuals better understand opinions on social media and make improved decisions.
what is ..how to process types and methods involved in data analysis
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
Sentiment analysis using machine learning and deep Learning
Sentiment analysis using machine learning and deep Learning
With the increasing rate at which data is created by internet users on various platforms, it becomes necessary to analyze and make use of the data by the Defense and other Government Organizations and know the sentiment of the people. This shall help the organizations take control of their actions and decide the steps to be taken shortly. Added to it, when something crucial is happening in the nation, it is of paramount importance to decide every step without hurting/violating the sentiments of the people. In the era of Microblogging, which has become quite a popular tool of communication, millions of users share their views and opinions on various day-to-day life issues concerning them directly or indirectly through social media platforms like Twitter, Reddit, Tumblr, Facebook. Data from these sites can be efficiently used for marketing or social studies. In this paper, we have taken into account various methods to perform sentiment analysis. Sentiment Analysis has been performed by using Machine Learning Classifiers. Polarity-based sentiment analysis, and Deep Learning Models are used to classify user's tweets as having `positive' or `negative' sentiment. The idea behind taking in various model architectures was to account for the variance in the opinions and thoughts existing on such social media platforms. These classification models can further be implemented to classify live tweets on twitter on any topic
Combining Lexicon based and Machine Learning based Methods for Twitter Sentim...
This document discusses combining lexicon-based and machine learning methods for Twitter sentiment analysis. It first describes lexicon-based approaches like TextBlob and Vader that use sentiment lexicons to determine tweet polarity. It then discusses machine learning approaches like random forest, support vector machines, and decision trees that are trained on labeled tweet data. The document finds that a random forest classifier achieved the highest accuracy of 99.92% at predicting tweet sentiment, demonstrating the effectiveness of combining both lexicon-based and machine learning methods for Twitter sentiment analysis.
Mental Health Monitor using facial expression
A Mental Health Monitor using facial expression technology is an innovative and empathetic tool designed to assess and support individuals' emotional well-being. By analyzing facial expressions in real-time, this advanced technology can detect various emotions such as happiness, sadness, anxiety, and stress.
Building a Sentiment Analytics Solution Powered by Machine Learning- Impetus ...
Building a Sentiment Analytics Solution Powered by Machine Learning- Impetus ...Impetus Technologies
This document discusses building a sentiment analysis solution powered by machine learning. It begins with an introduction to sentiment analysis and outlines the existing landscape of solutions. It then discusses challenges like accuracy and isolating content types. The document proposes that machine learning can help address these challenges by analyzing sentiment versus subjectivity, polarity reactions, and sentiment intensity. It describes how to build such a solution using machine learning, including creating a knowledge base and leveraging machine learning algorithms. Finally, it outlines Impetus Technologies' sentiment analysis solution and the benefits it provides.what is sentiment analysis.pptx
This PowerPoint presentation provides a concise introduction to sentiment analysis, exploring its role in analyzing emotions and opinions in text data. It covers the process of sentiment analysis, applications in customer experience, brand reputation, market research, and political analysis. The presentation highlights the benefits of sentiment analysis while acknowledging challenges in understanding context and ensuring data quality. It serves as a valuable resource for understanding the power of sentiment analysis in extracting insights from textual content.
SA
The document describes a sentiment analysis tool called STARK that analyzes user reviews of products. STARK can summarize what users talked about in a product, analyze sentiment towards specific features like cameras, show overall sentiment distributions, identify the most discussed features, and quantify sentiment scores for each feature. It addresses challenges in understanding context and uses natural language processing, sentiment scoring algorithms, and visualizations to help companies understand user feedback.
Political Prediction Analysis using text mining and deep learning.pptx
Social media platforms have vast users connected platforms like Twitter has 330 Million,
Facebook has 2.2 Billion, Google+ has 111 Million and LinkedIn has 467 Million which is
enough to create an impression through social media. We have proposed a system to
determine current sentiment on twitter using Twitter API for open access which includes
opinions from different content structures like latest news, audits, articles and social media
posts. And Deep Learning method to study Historic Data for predicting future results.
Previous implementations of prediction analysis on Twitter Data were not successful to fulfill
analysis on live Twitter Data. Thus, we have proposed a system which will predict the result
on live Twitter data and also generate the statistical graph which classify the polarity of
positive and negative tweets. With the help graphs, reports, trends and tweets one can predict
the future results of the political party and also can be used to create the campaigns.
A Review of machine learning approaches to mine Social Choice of voters.
This document presents a literature review of machine learning approaches used to mine social media data and predict voter choice or election results. It discusses how natural language processing techniques like tokenization and vectorization are used to structure social media data. Lexicon-based models and supervised learning algorithms like Naive Bayes, maximum entropy, logistic regression, and support vector machines are then used to analyze sentiment and predict if a user expresses positive, negative or neutral intent towards a candidate or policy. Deep learning models like convolutional and recurrent neural networks also show promise by identifying patterns in large unlabeled datasets. While current approaches predict elections accurately, the document suggests ensemble and deep learning models that can process more data could improve predictions of how voters' social media expressions correspond to their
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This letter, written by Kellen Harkins, Course Director at Full Sail University, commends Anny Love's exemplary performance in the Video Sharing Platforms class. It highlights her dedication, willingness to challenge herself, and exceptional skills in production, editing, and marketing across various video platforms like YouTube, TikTok, and Instagram.
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https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
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• Introduce a taxonomy for user goals with real world examples
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• Demonstrate the best approach to selection and prioritization of user-goals to address
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What You Need to Know About Social Media Sentiment Analysis
- 1. A Short Guide to Social Media Sentiment Analysis By Josephine Lester Broadstock youtube.com/@josephinelesterbroadstock
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- 3. Data Preprocessing: Clean and normalize the data by removing noise and irrelevant elements. Data Collection: Gather social media posts using APIs or web scraping.
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