In this NLP project, we applied Vader, a Natural Language Processing approach, to perform sentiment analysis on Twitter posts from individuals in Indiana, United States. Our goal was to gain insights into their opinions and emotions regarding the newly implemented legislation on gun ownership without a license.
To comprehensively understand the sentiments surrounding gun ownership, we conducted a two-phase examination. In the pre-implementation analysis, we observed negative reactions among individuals expressing concerns about the new legislation. However, in the post-implementation analysis, we found a notable decrease in negative sentiment. There were very few negative tweets indicating concerns about the law's impact, suggesting that public opinion became more positive after the implementation.
In addition to sentiment analysis, we also analyzed gun crime in Indiana after the law's implementation. The analysis revealed a decrease in the number of gun crime incidents, as evidenced by a reduction in tweets related to gun crime. This finding suggests a positive impact of the legislation, as fewer people were tweeting about gun crimes, indicating a potential decrease in such incidents.
Through our NLP project, we gained valuable insights into the sentiments and opinions of individuals in Indiana regarding the legislation on gun ownership without a license. The post-implementation analysis showed a notable decrease in negative sentiment, indicating a shift towards more positive public opinion. Furthermore, the analysis of gun crime tweets suggested a potential positive impact, with a decrease in the number of incidents mentioned on social media. These findings provide valuable information for policymakers and stakeholders in understanding the public response and potential impact of the legislation.
For more technical details about our project, feel free to check out my Github repository and LinkedIn Profile.
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