Our team completed the project in two weeks, delivering a fully functional, enhanced sentiment analysis tool with improved accuracy and additional features.
Bug Fixing of the Existing Sentiment Analysis Tool
- Objective: Identify and resolve the malfunctioning elements of the original tool to restore its core functionality.
- Challenges: the tool incorrectly categorized some comments, leading to unreliable results; the existing script was prone to crashing when processing large datasets or encountering unexpected Excel file formats.
- Solution: we carefully reviewed the existing codebase to locate logical and syntax errors that affected the tool’s performance; critical fixes included improving the text parsing methods and ensuring better handling of Excel file formats and edge cases (e.g., missing or malformed data); once the bugs were resolved, the sentiment categorization process became much more accurate and reliable, allowing The Gig Agency to process customer feedback again effectively.
Enhanced Functionality: Phrase Search Feature
- Objective: Add the ability for users to search for specific phrases within the comments to understand how certain topics or products were discussed across the feedback.
- Implementation: a new search functionality was integrated into the tool, allowing users to input specific phrases or keywords. The tool would then highlight and categorize the comments containing those phrases based on sentiment (positive, negative, or neutral); the search results provided a detailed breakdown of how often the phrase was associated with each sentiment category, helping the agency gain deeper insights into customer perceptions of crucial topics or campaigns.
Tracking Frequently Used Adjectives and Nouns by Sentiment
- Objective: Implement a counter that tracks and categorizes the most frequently used adjectives and nouns in the feedback, sorted by sentiment.
- Key Features: the tool was enhanced with natural language processing (NLP) capabilities to identify and extract nouns and adjectives from the comments. These were then grouped based on sentiment (positive, negative, neutral); the tool tracked how often specific adjectives and nouns appeared in the feedback and displayed them in a report, allowing the agency to understand the most common descriptors and themes used by their customers; each term was associated with the most frequently used sentiment category, which gave the agency a clear view of how customers described their experiences positively and negatively.
- Business Impact: This feature allowed The Gig Agency to gain more granular insights into customer sentiment by identifying key patterns in product or service descriptions, enabling more informed marketing strategies.
Challenges and Solutions
- Script Performance: One challenge was ensuring the tool could handle large datasets without slowing down or crashing. We optimized the existing code and streamlined data parsing processes to improve speed and reliability when processing large volumes of comments.
- Sentiment Accuracy: Another challenge was improving sentiment classification accuracy, particularly for comments with mixed or ambiguous language. We refined the algorithm's ability to correctly detect positive, negative, or neutral tones by updating the sentiment lexicon and adding a more nuanced handling of multi-sentiment phrases.
Outcome
The project was completed within two weeks, delivering a fully functional and enhanced sentiment analysis tool for The Gig Agency. The tool was restored to its core functionality of categorizing customer comments by sentiment. At the same time, new features such as the phrase search and adjective/noun frequency tracking provided the agency with deeper insights into customer feedback.
