How can you use sentiment analysis to monitor public opinion about your community organizing initiatives?

Some years ago we built a sentiment analysis engine to search twitter and graph the results of tweets. This was during an election year and we could watch the effects of the Cambridge Analytica effort to influence the swing voters through bogus accounts on face book. The measure of sentiment was a novelty to most people - who simply searched thier favorite celebrities or terrestrial sports team players. I practiced as a professional counselor for a decade - let me tell you even micro expressions can tell you what lurks in the sentiments of the people you are there to help. Sentiment analysis can do more than a simple positive or negative. It can measure colors of affect and emotion depending on its tuning.

I'd like to highlight that this falls under the domain of NLP. Prior to the LLM hype, more than a year ago, my colleague and I successfully deduced the scoring (KPI) of bot-assisted conversations with customers for a telephone company. We achieved this by analyzing polarity and emotional tone. It was an enjoyable experience, and I can't imagine how to face it without the context of sentiment analysis.

Sentiment analysis interprets emotional tone in text, classifying it as positive, negative, or neutral. It assigns numerical scores, categorizes sentiments, and operates at various levels. Applied in assessing public opinion, it aids decision-making and improves stakeholder satisfaction. Sentiment analysis monitors public opinion on community initiatives through real-time tracking, trend analysis, and feedback extraction. It informs adaptive communication, identifies influential factors, and supports data-driven decision-making for enhanced community engagement and positive sentiment.

Overall, sentiment analysis provides a data-driven approach to understanding and adapting to public opinion, enhancing community engagement, and fostering positive sentiment. It enables organizers to be responsive, adaptive, and proactive in building strong community support.

As of the current date, numerous companies provide mature out-of-the-box solutions. If your requirements align with factors such as no concerns about data privacy, maintaining a specific information structure, and utilizing English text, one of these companies may suffice for your needs. However, if the volume of information doesn't surpass the cost-benefit threshold, and you operate within a specific communication domain where the text isn't in the lingua franca (e.g. Brazilian Portuguese or Mexican Spanish), it could be beneficial to consider a model like BART/BERT. Creating a tailored pipeline and fine-tuning the model can yield optimal results for your specific use case.

Several limitations are associated with this mechanism, including: - Not understanding sarcasm. - Standardized textual expressions: adaptability to diverse linguistic styles. - The language used must adhere to a wordy style. - Use of emojis. - Interpretation of interjections: like 'umm,' 'ahhh,' 'wow!,' or 'oh-oh'. - Accessibility in low-resource languages. It is important to note that emotion recognition is a comprehensive field encompassing both written and spoken language. Unlike the written language-dependent limitations mentioned earlier, emotion recognition in spoken language considers factors such as tone and prosody, allowing for the identification of elements like sarcasm independently of written language nuances.