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https://github.com/sakthivinash2/sentiment-analysis-of-youtube-comments

Sentiment analysis of YouTube comments unlocks a treasure trove of insights into audience perception. This technique, also known as opinion mining, delves into the emotional undercurrents of comments, categorizing them as positive, negative, or neutral.
https://github.com/sakthivinash2/sentiment-analysis-of-youtube-comments

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Sentiment analysis of YouTube comments unlocks a treasure trove of insights into audience perception. This technique, also known as opinion mining, delves into the emotional undercurrents of comments, categorizing them as positive, negative, or neutral.

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# sentiment-analysis-of-YouTube-comments

Sentiment analysis of YouTube comments unlocks a treasure trove of insights into audience perception. This technique, also known as opinion mining, delves into the emotional undercurrents of comments, categorizing them as positive, negative, or neutral.



## Introduction:

* Public perception of recycling is crucial for its success. However, gauging public opinion and understanding what resonates with viewers can be challenging. 

* By scraping comments from diverse recycling videos (educational explainers, personal hauls, news reports), we can train a sentiment analysis model to classify comments as positive, negative, or neutral towards recycling. Analyzing this data can reveal the overall sentiment and recurring themes for different video types. This allows content creators to tailor messages, identify communication gaps, and understand what aspects of recycling resonate most with viewers.



## System Development Approach:

1. Data Collection:

* Target Source: We'll focus on Ken Jee's YouTube channel dedicated to data science and analytics. This provides a controlled environment with a target audience likely interested in data analysis.

* API or Script: Utilize the YouTube Data API (v3) or a web scraping script to collect comments from relevant videos on Ken Jee's channel.

* Dataset used: [click here](data_set/ "Dataset used title")

2. Data Preprocessing:

* Cleaning: Remove irrelevant information like punctuation, usernames, URLs, and emojis.

* Normalization: Convert text to lowercase and handle typos/abbreviations.

* Stop Word Removal: Eliminate common words that don't contribute to sentiment analysis (e.g., "the," "a").

* Stemming/Lemmatization: Reduce words to their root form (e.g., "running" becomes "run").

3. Exploratory Data Analysis (EDA):

* Analyze comment volume distribution across different recycling video types (e.g., educational, opinion-based).

* Identify common words and phrases used in the comments.

* Explore the sentiment distribution (positive, negative, neutral) across different videos.

4. Sentiment Analysis:

* Model Selection: Choose a sentiment analysis model (Lexicon-based or Machine Learning) suitable for short text like comments.

* Training: Train the model on a labeled dataset of recycling-related comments with positive, negative, or neutral sentiment. This dataset can be created manually or sourced from public repositories.

* Evaluation: Evaluate the model's performance on a separate test dataset to ensure accuracy.

5. Analysis and Visualization:

* Analyze the sentiment distribution of comments across different video types.

* Identify recurring themes and keywords associated with positive and negative sentiment.

* Create visualizations (e.g., word clouds, sentiment bar charts) to showcase key findings.

6. Deployment (Optional):

* Develop a web application or dashboard to display the sentiment analysis results in real-time or periodically.

* This allows Ken Jee to monitor audience perception and adjust content strategy accordingly.

7. Evaluation:

* Continuously monitor the model's performance and retrain it with new data to maintain accuracy.

* Evaluate the impact of the system on Ken Jee's content strategy and audience engagement.

8. Results:

* Overall Sentiment: Understand the general public's perception of recycling on Ken Jee's channel (positive, negative, or neutral).

* Content Impact: See how viewers respond to different video types (educational vs. personal) and identify topics triggering stronger positive/negative reactions.

* Key Audience Concerns: Discover the recurring themes and concerns viewers have about recycling through comment analysis.

* Effective Messaging: Identify the keywords and phrases associated with positive sentiment to tailor content that resonates with the audience.



## Future Scope:

* Go Beyond Ken Jee: Analyze comments from a wider range of YouTube channels for a broader view of public perception on recycling.

* Deeper Sentiment Analysis: Use advanced models to capture more nuanced emotions (frustration, confusion) and identify key themes through topic modeling.

* Comparative Insights: Compare sentiment across channels and video types to understand how content creators shape audience perception. Track changes for Ken Jee to measure the impact of his strategy.

* Predictive Power: Develop models to predict audience sentiment based on video content and use them to tailor content for maximum positive impact.



## Conclusion:

* This exploration of sentiment analysis on Ken Jee's recycling video comments has shed light on public perception of recycling. Analyzing viewer sentiment revealed the general support for recycling on the channel, the effectiveness of different video formats, and key audience concerns. This empowers Ken Jee to tailor his content, address viewer anxieties, and craft messages that resonate most effectively.



* The future holds exciting possibilities. Expanding the analysis to encompass a wider range of channels can offer a more comprehensive understanding of public sentiment. Additionally, exploring advanced sentiment analysis models can unlock nuanced emotions and recurring themes within comments. This knowledge, combined with comparative analysis and predictive modeling, empowers creators to anticipate audience reactions and optimize content for maximum impact on recycling awareness.