Sentiment Analysis is the task of determining the sentiment (positive, negative, or neutral) expressed in a given piece of text. In this project, Sentiment Analysis using NLP in C, we focus on creating a simple sentiment analysis tool using C programming. The goal is to determine whether a given text (such as a sentence or paragraph) expresses a positive, negative, or neutral sentiment based on predefined word sentiments.

Key Features of the Project:

1.Sentiment Lexicon:

• A predefined list of words, each associated with a sentiment value.
• Positive words (e.g., "happy", "good") are assigned a sentiment score of +1, while negative words (e.g., "sad", "bad") have a sentiment score of -1.
• Words that don’t appear in the lexicon are ignored, and the sentiment score remains unaffected.

2.Text Preprocessing:

• The input text is first tokenized into individual words (using spaces as delimiters).
• Each word is converted to lowercase to ensure case-insensitive matching with the lexicon.

3.Sentiment Score Calculation:

• For each word in the input text, the program checks whether the word exists in the sentiment lexicon.
• If a match is found, the corresponding sentiment score (positive or negative) is added to the overall sentiment score for the text.

4.Classification of Sentiment:

Based on the total sentiment score:

• A positive score indicates positive sentiment.
• A negative score indicates negative sentiment.
• A score of zero or near zero indicates neutral sentiment.

5.User Interaction:

• The user inputs a text string (sentence or paragraph).
• The program processes the text and outputs the sentiment classification (Positive, Negative, or Neutral).

How the Project Works:

• Input: The user is prompted to enter a piece of text (a sentence or multiple sentences).
• Preprocessing: The input text is tokenized into individual words, and each word is converted to lowercase.
• Lexicon Matching: Each word is compared against a predefined lexicon of positive and negative words. If a match is found, the respective sentiment score is added to the total sentiment score.
• Sentiment Calculation: The total sentiment score is calculated based on the matches in the lexicon.
• Sentiment Classification: Based on the score, the sentiment is classified as positive, negative, or neutral, and the result is displayed to the user.

Technologies and Concepts Used:

• C Programming: The entire implementation is done in C, using standard libraries.
• Text Tokenization: The process of splitting the input text into individual words for analysis.
• Lexicon-Based Approach: A simple approach to sentiment analysis using a predefined list of words and their associated sentiment values.
• String Handling: Functions like strtok() for tokenizing and tolower() for case conversion are used for text processing.

Limitations:

• Small Lexicon: The lexicon used is small and might not capture the full complexity of natural language sentiment. A real-world implementation would require a much larger lexicon or a more sophisticated model.
• Lack of Context Understanding: The system does not understand context or handle nuances such as sarcasm or negations (e.g., "not good" would not be recognized correctly).
• Manual Approach: The sentiment analysis is based on predefined rules and word matching, unlike machine learning-based approaches that can learn from data.

Potential Improvements:

• Expanding the Lexicon: The lexicon could be extended to include more words, improving the accuracy of sentiment analysis.
• Contextual Understanding: A more advanced approach could incorporate machine learning models that understand the context, sarcasm, and word relationships.
• Handling Negations: Implementing a system to detect negations (e.g., "not good") could improve accuracy.
• Performance Optimization: For larger datasets, performance optimizations could be implemented, such as using more efficient data structures for the lexicon.

Conclusion:

The Sentiment Analysis using NLP in C project is a simple yet effective way to perform sentiment classification using basic Natural Language Processing techniques. It relies on a sentiment lexicon and tokenizes the input text to compute a sentiment score, classifying the text as positive, negative, or neutral. This project serves as an introduction to sentiment analysis in C, providing a solid foundation for more complex NLP tasks in the future. However, for more advanced use cases, leveraging higher-level languages like Python, with libraries such as NLTK or SpaCy, would be preferable.