Sentiment Analysis

Sentiment Analysis (Lexicon-Based Polarity Quantification)

Authors:

• Amey Thakur (ORCID: 0000-0001-5644-1575)
• Mega Satish (ORCID: 0000-0002-1844-9557)

Release Date: January 9, 2022
License: MIT License

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Quick Start

To execute this implementation, install the required dependency and run:

pip install -r requirements.txt
python SentimentAnalysis.py

1. Definition

Sentiment Analysis (or Opinion Mining) is a subfield of Natural Language Processing (NLP) that involves the computational identification and categorization of opinions expressed in text. It aims to determine the writer's attitude toward a particular topic or the overall contextual polarity of a document.

2. Mathematical Explanation

The Compound Score in VADER is calculated by summing the valence scores of each word in the lexicon, adjusted according to rules, and then normalized to be between -1 (extreme negative) and +1 (extreme positive).

The normalization formula is:

$$ x_{norm} = \frac{x}{\sqrt{x^2 + \alpha}} $$

Where:

• $x$ is the sum of the valence scores of the words.
• $\alpha$ is a constant (typically 15).

A sentence is classified as:

• Positive: $Score \ge 0.05$
• Neutral: $-0.05 < Score < 0.05$
• Negative: $Score \le -0.05$

3. Computer Science Theory

• Lexicon-Based Approach: Relies on a pre-built dictionary of words tagged with their emotional intensity (valence).
• Rule-Based Heuristics: Handles linguistic complexities that simpler bag-of-words models miss:
  • Negation Conversion: "not good" flips the polarity.
  • Intensifier Adjustment: "extremely good" increases valence intensity.
  • Contrastive Conjunctions: "but" often shifts the sentiment weight to the second half of the sentence.
• VADER Performance: Specifically tuned for social media and short-text forensics, where emojis and capitalization ("GREAT!") carry significant weight.

4. Python Implementation Logic

• SentimentAnalysisService: Encapsulates the NLTK SentimentIntensityAnalyzer for a clean, scholarly interface.
• Resource Management: Automatically downloads the vader_lexicon if missing from the local environment.
• Polarity Mapping: Returns a granular dictionary containing raw positive, negative, and neutral ratios alongside the normalized compound score.

5. Visual Representation

Forensic Sentiment Dashboard

The analytic engine provides real-time polarity quantification across multiple linguistic dimensions.

graph TD
    Input["Input Text"] --> Token["Tokenization & POS Tagging"]
    Token --> Lexicon["Valence Lookup (Lexicon)"]
    Lexicon --> Rules["Heuristic Rules Application"]
    Rules --> Neg["Negation Handler"]
    Rules --> Int["Intensifier Handler"]
    Neg --> Sum["Lexical Polarity Sum"]
    Int --> Sum
    Sum --> Normal["Normalization (α=15)"]
    Normal --> Result["Compound Score (-1 to +1)"]
    Result --> Label["Sentiment Label"]

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