Twitter Trend Analysis Using Latent Dirichlet Allocation

Twitter Trend Analysis Using Latent Dirichlet Allocation

Abstract:

Twitter Trend Analysis Using Latent Dirichlet Allocation studies social trends on Twitter. Social trends show a popular topic or event. This project investigates the causes of these trends and proposes a typology of news, ongoing events, memes, and commemoratives. This project’s system lets users search for trends by keyword. It finds similar keywords in a database and summarizes Twitter’s trending tweets. Trending hashtagged tweets are displayed first, followed by related tweets. Clicking trending tweets displays user comments. Users can also view all keyword-related tweets.

Introduction:

Social media has transformed communication and sharing. Twitter, a popular microblogging platform, lets users tweet their thoughts and opinions. Twitter users’ behavior often creates social trends, which indicate a topic or event’s popularity at a given time. Understanding these trends’ causes can reveal Twitter users’ preferences.

Objectives:

This project develops an LDA-based Twitter trend analysis system. LDA, a probabilistic topic modeling model, can identify underlying topics and themes in a large tweet collection. We use LDA to find the news, events, memes, and commemoratives that cause Twitter social trends. Users can also search for trending tweets by keyword, giving them a complete view of their interests.

Project Details:

The Twitter Trend Analysis system will be built using various technologies and components. The project will involve the following steps:

1. Data Collection: The system will utilize Twitter’s API to gather a large volume of tweets. These tweets will serve as the dataset for trend analysis.
2. Preprocessing: The collected tweets will undergo preprocessing steps such as tokenization, stop word removal, and stemming. This process will help clean the data and prepare it for analysis.
3. Latent Dirichlet Allocation (LDA): The preprocessed tweets will be fed into the LDA model, which will identify the underlying topics within the dataset. LDA will assign probabilities to each tweet for belonging to different topics.
4. Trend Identification: Based on the topics generated by LDA, the system will identify the trending tweets related to specific keywords. The system will summarize the total count of tweets associated with each trending topic.
5. Display of Trending Tweets: The system will display the trending tweets, giving priority to those with hashtags (#) to highlight their popularity. Users can click on each trending tweet to view associated user comments and opinions.
6. Keyword Search: Users will have the option to search for specific trends by entering a keyword. The system will search for similar keywords in the database and present the relevant trending tweets.

Conclusion:

Twitter Trends Latent Dirichlet Allocation is a complete Twitter social trend analysis tool. LDA and a tweet database let users explore the latest trends based on their interests. The project’s typology of triggers—news, ongoing events, memes, and commemoratives—categorizes Twitter trends. This project advances social media analysis by revealing Twitter users’ collective behavior.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.