Exploring Text Classification Capabilities of Language Models for Topic, Sarcasm, Genre, and Sentiment Analysis

Large language models (LLMs) have revolutionized the field of natural language processing (NLP) with their ability to perform tasks with high levels of efficiency and accuracy.

One such powerful application of LLMs is text classification, which rapidly categorises textual data into predefined categories related to topics, genres, and sentiments.

This blog post will delve into the world of text classification with LLMs. We will discuss their effectiveness in categorizing text and examine five real-life examples that showcase text classification’s practical applications and benefits.

The Significance of Text Classification

Text classification is essential for a variety of reasons, mainly because it allows us to:

• Organize and manage large volumes of textual data
• Automate processes and improve overall efficiency
• Facilitate content discovery and analysis

Companies across many industries require text classification capabilities for diverse applications, including spam filtering, sentiment analysis, and content recommendations.

Using LLMs for Text Classification

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LLMs work by analyzing vast amounts of text and learning linguistic structures and patterns. This enables them to complete various NLP tasks, including text classification, with remarkable results. LLMs surpass traditional text classification methods, often relying on keyword matching, rule-based systems, or manual annotation, providing more accurate and efficient outcomes.

Text Classification Categories

Some specific categories in text classification are:

• Topic/Subject Classification: Organizing text based on its subject or theme
• Genre Classification: Grouping text based on style or format, such as literary genres or movie genres
• Sentiment Analysis : Assessing the sentiment, emotion, or opinion expressed in a text, typically as positive, negative, or neutral
• Irony Detection: Like sarcasm, irony involves saying something but meaning the opposite. However, irony is often more subtle and less bitter than sarcasm. Detection of irony in text consists in understanding the context and the intended meaning behind the words.
• Humor Detection involves determining whether a text is intended to be humorous. Humor, like sarcasm, often relies on context, wordplay, and unexpected twists, making it challenging for machine learning models.
• Satire Detection: Satire is a form of humor that uses irony, sarcasm, or ridicule to criticize or comment on society, politics, or human nature. Detecting satire in text involves understanding the nuances of language and the social and political context.
• Lie Detection: involves determining whether a statement is true or false. Like sarcasm detection, lie detection requires understanding the context and the subtle cues that might indicate deception.
• Offensive Language Detection: Identifying whether a text contains offensive or abusive language. Like sarcasm, offensive language can be subtle and context-dependent, making it challenging for machine learning models.

That is only a selection of possible text-classification tasks.

Application Examples

Here are five practical examples of text classification applications in real-life scenarios:

• Sorting Customer Reviews into Positive, Negative, and Neutral Categories: Allowing businesses to analyze customer feedback and make necessary adjustments to enhance products, services, and customer satisfaction
• Sorting News Articles by Topics, such as Politics, Sports, or Entertainment: Improving content discovery and personalization for news websites and aggregators
• Sorting Emails into Spam and Non-Spam folders: Reducing inbox clutter and protecting users from malicious content
• Sentiment Analysis for Social Media Monitoring: Assessing public opinion and detecting trends, critical for industries like marketing, public relations, and politics
• Genre Classification for Book Recommendations: Facilitating personalized book suggestions by online bookstores and libraries to enhance user experience and drive engagement

Prompts

Test Text used for this example prompts:

Sort this text by topic: - Sorting Customer Reviews into Positive, Negative, and Neutral Categories: This allows businesses to analyze customer feedback and make necessary changes to improve products, services, and customer satisfaction. - Sorting News Articles by Topics, such as Politics, Sports, or Entertainment: Improving content discovery and personalization for news aggregators and websites

Filtering email into Spam and Non-Spam folders: Sentiment Analysis for Social Media Monitoring: Reducing inbox clutter and protecting users from malicious content Detecting trends and assessing public opinion, which is critical in industries such as marketing, public relations, and politics - Genre Classification for Book Recommendations: Personalized book recommendations by online bookstores and libraries to improve user experience and drive engagement

Prompt by topics:

Classify this text by topics: {test text}

Customer Feedback Analysis Content Personalization and Discovery Email Filtering Social Media Sentiment Analysis Book Recommendation Systems

Prompt by genre:

Classify this text by genre: {test text}

Business and Customer Service News and Media Personalization Email Management and Security Social Media Analytics and Public Relations Literary Recommendation Systems

Prompt by genre and sentiment:

Classify this text by genre and sentiment: {test text}

Genre:

Business and Marketing Technology and Media Email Management Social Media Analysis Personalization and Recommendation Systems Sentiment: Neutral

Prompt by genre and topic, returning a sentiment table as markdown:

Classify this text by genre and topic, and create a table where the genre is the row name, and the topic is the column name, and the value is the sentiment value, the table should be returned as markdown code: {test text}

Generic prompt: Text categorization is a synonym for text classification.

text categorization of: Aspect-based sentiment analysis is considered an intermediate-to-advanced NLP task. It requires a higher level of language understanding and processing than core NLP tasks. It builds upon basic sentiment analysis, which is an intermediate NLP task, by identifying and determining the sentiment towards specific aspects or features mentioned in a text, rather than providing an overall sentiment.

The complexity of aspect-based sentiment analysis can vary depending on the techniques and models used, as well as the domain and context of the analyzed text. In some cases, it can be closer to an advanced NLP task, particularly when dealing with complex text structures or when a deeper understanding of the language and its nuances is required.

Result:

The text can be categorized under “Artificial Intelligence” or “Natural Language Processing,” as it discusses aspect-based sentiment analysis, a specific NLP task, and its complexity in relation to other NLP tasks.

Conclusions

Text classification with LLMs has already demonstrated its potency and reliability, enabling the accurate categorization of textual data into a wide array of predefined classes.

As NLP continues to evolve, text classification with LLMs will undoubtedly expand, delivering ever-more advanced solutions to tackle the challenges of processing and understanding the vast amounts of daily textual data.

With applications that span subject classification, genre classification, and sentiment analysis, text classification with LLMs is becoming an essential tool for businesses and researchers seeking deeper insights and more efficient management of their textual data resources.