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Opinion Mining on Transliterated TweetsBy Umer NasimDepartment of Computer Sciences QuaidiAzam University Islamabad, Pakistan February, 2017Opinion Mining on Transliterated TweetsBy Umer NasimSupervised
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Identify the text data you want to analyze for sentiment.
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Choose the sentiment analysis tool or method that suits your needs.
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Preprocess the text data by cleaning it, removing stop words, and normalizing.
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Segment the text into sentences or phrases for better analysis.
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Apply the sentiment analysis algorithm to classify the text as positive, negative, or neutral.
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Aggregate the results to derive overall sentiments, trends, or opinions.
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Interpret the results to make informed decisions or insights.

Who needs opinion mining and sentiment?

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Businesses looking to gauge customer satisfaction.
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Researchers analyzing public opinion.
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Politicians monitoring sentiments on policies or campaigns.
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Product developers understanding user feedback.
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Opinion mining, also known as sentiment analysis, is the process of analyzing text data to determine the sentiment expressed within it, such as positive, negative, or neutral feelings about a subject.
Individuals and organizations that conduct large-scale analyses of public feedback, reviews, or social media data are typically required to file sentiment analyses related to their business or research purposes.
To fill out sentiment analysis reports, one should gather relevant data, process it using sentiment analysis tools, summarize the findings, and then document the results according to the specified reporting format.
The purpose of opinion mining and sentiment analysis is to understand public perceptions, monitor brand reputation, improve customer satisfaction, and inform decision-making processes.
The report should include the methodology used for analysis, data sources, a summary of sentiment scores, key insights, and any recommendations based on the findings.
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