What is Sentiment Analysis

Sentiment analysis is the computational process of identifying and quantifying subjective information—such as opinions, emotions, and attitudes—from text data. It is widely used in natural language processing (NLP) to determine whether a piece of writing is positive, negative, or neutral. By analyzing language patterns, sentiment analysis helps businesses, researchers, and organizations understand public perception, customer feedback, or social media trends. For example, companies often use sentiment analysis to gauge customer satisfaction from product reviews or social media comments, enabling them to make data-driven decisions and improve their services or products.

Sentiment Analysis in Voyant Tools

Voyant Tools offers features for sentiment analysis through automated tagging of words, particularly adjectives, with positive or negative connotations. This process involves assigning sentiment scores to words based on predefined lexicons, allowing users to visualize the emotional tone of a text corpus. However, the automated tagging in Voyant Tools often requires fine-tuning to ensure meaningful analysis. Without careful adjustment, the results may include false positives or misclassifications, especially in nuanced or context-dependent texts. Users must manually review and refine the sentiment dictionaries or adjust the analysis parameters to improve accuracy and relevance.

Other Tools for Sentiment Analysis

Beyond Voyant Tools, there are numerous platforms and programming libraries designed for sentiment analysis. Code-based tools for sentiment analysis include TextBlob in Python and provide access to pre-trained models and lexicons. A sentiment analysis tool with a graphic user interface to be run in browsers is Bert XY’s SENSOR (SentimEnt aNalysiS Of Reviews) platform designed at Hoge School Zuyd to introduce students to sentiment analysis, focusing on customer reviews. The tool allows users to input review data and automatically analyzes the sentiment by tagging words — primarily adjectives — with positive or negative connotations. This process is based on predefined sentiment lexica, which classify words according to their emotional tone. The platform is intentionally kept simple to highlight both the potential and the limitations of automated sentiment analysis. Students are encouraged to critically assess the results, identify inaccuracies, and consider how the analysis could be improved. The tool also includes supplementary resources to guide students through the process.