Sentiment and Tone Analysis of Reddit Posts Using Python

The volume of user-generated content that the social media space, particularly that of Reddit, produces is unprecedented, which now can be considered an excellent source of analysis for the study of public opinion and communication trends. However, such huge unstructured text pieces also pose several problems for analytics that are exacerbated in the presence of noise in the texts, non-standard variations of language usage, sarcasm, and so forth, as well as the requirements for scalable modes of computation. Although new advances in Natural Language Processing(NLP) like BERT and RoBERTa do much better than their previous transformer-based models, at a great computational cost, there is also an enormous labeled dataset to fine-tune. Light in weight but falling flat where the situation might be ambiguous based on the context or subtlety in the expression, there are lexicon-based traditional methods like VADER and TextBlob. A Python-based system that uses advanced NLP techniques for sentiment and tone analysis of Reddit posts. The study categorizes the sentiments as positive, negative, and neutral. The tones were also kept in mind, differentiating formal posts from informal ones. The methodology undertaken was a step-by-step approach, fetching data from the Reddit API, preprocessed removal of noise, and text that is absolutely irrelevant. VADER and TextBlob have been employed to perform sentiment analysis. Developing a tone detection method depends on linguistic features such as vocabulary complexity, sentence structure, and punctuation usage. It was written with respect to computational efficiency and scalability, using Python libraries such as NumPy, NLTK, MatplotLib and spaCy. This work contributes to social media analytics through an inclusive and scalable framework for potential application in analyzing complex unstructured textual data. This maps the generalization to brand reputation management, public policy planning, and content management activities, all of which require user sentiment and tone as crucial aspects of making a correct decision.