Unsupervised Aspect Based Sentiment Analysis

Unsupervised Aspect-Based Sentiment Analysis (ABSA) is a natural language processing (NLP) task that aims to identify and analyze the various aspects or facets of a given text (usually customer reviews, social media posts, or product descriptions) and determine the sentiment associated with each aspect. Unlike supervised ABSA, where you have labeled training data with aspects and their corresponding sentiments, unsupervised ABSA does not rely on such labeled data and seeks to discover aspects and sentiments in an unsupervised manner.

Key Elements:

Aspect Extraction: Unsupervised ABSA leverages unsupervised algorithms, such as topic modeling or clustering, to identify aspects or topics within textual data. These aspects represent specific attributes or dimensions being discussed in the text.

Sentiment Analysis: Once aspects are identified, the system performs sentiment analysis at the aspect level. This involves assessing the sentiment polarity (positive, negative, neutral) associated with each aspect. Techniques like lexicon-based sentiment analysis and sentiment propagation are commonly used.

Aspect-Level Aggregation: Sentiment scores for individual aspects are aggregated to provide an overall sentiment assessment for the entire text. Various aggregation methods can be employed, depending on the context and requirements.

Applications:

Unsupervised ABSA finds application in a wide range of domains, including:

Customer Reviews: It helps businesses understand customer opinions by dissecting feedback into aspects like product quality, customer service, and pricing, along with sentiment analysis.

Social Media Analysis: Analyzing social media conversations by identifying key topics and sentiments associated with brands, products, or events.

Market Research: Gaining insights into market trends and consumer preferences by analyzing unstructured textual data from surveys, forums, or reviews.

Challenges:

Lack of Labeled Data: Unsupervised ABSA does not rely on labeled data, making it versatile but potentially less accurate than supervised approaches.

Aspect Granularity: Determining the appropriate granularity level for aspects can be challenging.

Negations and Modifiers: Handling negations (e.g., "not good") and modifiers that influence sentiment is a complex task.

Contextual Ambiguity: Disambiguating words or phrases with multiple meanings based on context can be challenging.

Python Environment
Libraries:
nltk
spacy
genism
scikit-learn
matplotlib
seaborn
wordcloud