Here’s a step-by-step breakdown of how to implement a lexicon-based sentiment analysis strategy:

1. Understanding Lexicon-Based Sentiment Analysis

• Definition: A lexicon-based approach relies on a predefined list of words or phrases, each assigned a sentiment score or category.
• Purpose: To quantify the sentiment expressed in a given text by analyzing the occurrence and strength of sentiment-laden words.

2. Selecting a Lexicon

• Choose a Lexicon: Select a suitable sentiment lexicon based on the context of the analysis. Common lexicons include:
  • SentiWordNet: A lexical resource for sentiment analysis that assigns sentiment scores to WordNet synonyms.
  • VADER (Valence Aware Dictionary and sEntiment Reasoner): Specifically designed for social media text, providing a rule-based sentiment analysis approach.
  • LIWC (Linguistic Inquiry and Word Count): A psychological lexicon that includes sentiment-related words and their categories.

3. Data Collection

• Gather Text Data: Collect the textual data you want to analyze (e.g., tweets, reviews, articles).
• Data Cleaning: Preprocess the data by removing unnecessary elements such as HTML tags, URLs, and special characters.

4. Text Preprocessing

• Tokenization: Break the text into individual words or tokens.
• Normalization: Convert text to lowercase to ensure consistency and facilitate matching with lexicon entries.
• Stop Word Removal: Remove common words (like "the," "is," etc.) that do not contribute significant sentiment information.

5. Sentiment Scoring

• Word Matching: Compare each tokenized word against the selected lexicon. For each match:
  • Assign Scores: Retrieve the sentiment score from the lexicon (e.g., +1 for positive words, -1 for negative words).
  • Consider Context: In some cases, words may have different meanings based on context; apply rules or modifiers to adjust scores accordingly (e.g., negation, intensifiers).
• Calculate Overall Sentiment:
  • Aggregate Scores: Sum the sentiment scores for all matched words in the text to obtain an overall sentiment score.
  • Categorize Sentiment: Determine the sentiment category (positive, negative, neutral) based on the total score (e.g., positive if score > 0, negative if score < 0).

6. Handling Negations and Modifiers

• Negation Handling: Implement rules to modify sentiment scores when negations are present (e.g., "not happy" should lead to a negative score).
• Intensity Modifiers: Adjust scores based on intensifiers (e.g., "very good" could increase the positive score) or diminutive modifiers (e.g., "somewhat bad" could decrease the negative score).

7. Testing and Validation

• Test on Sample Data: Apply the lexicon-based analysis to a sample of data and validate the results against expected sentiments.
• Adjust Lexicon and Rules: Fine-tune the lexicon and scoring rules based on validation results to improve accuracy.

8. Visualization of Results

• Data Presentation: Visualize the sentiment analysis results using charts or graphs (e.g., pie charts, bar graphs) to summarize overall sentiment distribution.
• Insights Generation: Interpret the visualizations to gain insights into trends, sentiment changes over time, or comparisons across categories.

9. Continuous Improvement

• Update Lexicons: Regularly update the lexicon to include new words or phrases that may emerge, particularly in dynamic fields like social media.
• Feedback Mechanism: Implement a system for users to provide feedback on sentiment accuracy, which can help refine the lexicon and rules.

10. Integration with Other Techniques

• Combine with Machine Learning: Consider integrating the lexicon-based approach with machine learning techniques to enhance accuracy and account for context.
• Multi-Modal Analysis: Use the lexicon approach alongside other sentiment analysis methods (e.g., rule-based, machine learning) for a comprehensive analysis.