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Topic Based Sentiment Analysis on Twitter
BY
QUAD SOON YIN GA project report submitted in partial fulfillment of the
requirements for the award of Bachelor of Science (Hons.)
Applied Mathematics with
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How to fill out topic based sentiment analysis
How to fill out topic based sentiment analysis
01
Step 1: Identify the topic for which you want to perform sentiment analysis.
02
Step 2: Collect relevant data or text documents related to the chosen topic.
03
Step 3: Preprocess the data by removing any irrelevant information, such as stop words or special characters.
04
Step 4: Choose a sentiment analysis algorithm or tool that suits your requirements.
05
Step 5: Apply the chosen algorithm or tool to analyze the sentiment of each document or text in relation to the topic.
06
Step 6: Generate sentiment scores or labels for each document or text. These scores can be positive, negative, or neutral.
07
Step 7: Use the sentiment scores or labels to gain insights into the overall sentiment towards the topic.
08
Step 8: Analyze the results and draw conclusions based on the sentiment analysis.
09
Step 9: Optionally, visualize the results using charts or graphs to better understand the sentiment distribution.
10
Step 10: Iterate and refine the sentiment analysis process based on the feedback or insights gained.
Who needs topic based sentiment analysis?
01
Market researchers who want to understand consumer sentiment toward specific topics or products.
02
Brands or businesses looking to gauge customer satisfaction or sentiment about their services or products.
03
Social media analysts who want to monitor public opinion on various topics or events.
04
Political analysts who want to measure public sentiment toward politicians or policy decisions.
05
Academic researchers analyzing sentiment in specific domains or industries.
06
Product managers or developers interested in user feedback and sentiment analysis for product improvement.
07
Customer support teams who want to understand customer sentiment and identify areas for improvement.
08
Financial analysts interested in sentiment analysis of stock market news or investor sentiment.
09
Media organizations analyzing public sentiment toward news stories or events.
10
Online review platforms looking to automatically categorize and analyze sentiment in user reviews.
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