Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts (1412.5404v1)

Abstract: The short text has been the prevalent format for information of Internet in recent decades, especially with the development of online social media, whose millions of users generate a vast number of short messages everyday. Although sophisticated signals delivered by the short text make it a promising source for topic modeling, its extreme sparsity and imbalance brings unprecedented challenges to conventional topic models like LDA and its variants. Aiming at presenting a simple but general solution for topic modeling in short texts, we present a word co-occurrence network based model named WNTM to tackle the sparsity and imbalance simultaneously. Different from previous approaches, WNTM models the distribution over topics for each word instead of learning topics for each document, which successfully enhance the semantic density of data space without importing too much time or space complexity. Meanwhile, the rich contextual information preserved in the word-word space also guarantees its sensitivity in identifying rare topics with convincing quality. Furthermore, employing the same Gibbs sampling with LDA makes WNTM easily to be extended to various application scenarios. Extensive validations on both short and normal texts testify the outperformance of WNTM as compared to baseline methods. And finally we also demonstrate its potential in precisely discovering newly emerging topics or unexpected events in Weibo at pretty early stages.

• The paper introduces the Word Network Topic Model (WNTM) that models topics over words using a word co-occurrence network to effectively handle short, sparse, and imbalanced texts.
• Experiments show WNTM outperforms traditional methods like LDA in topic coherence, semantic similarity, and detecting rare topics, especially in microblog data.
• WNTM holds promise for real-time analysis on platforms like Twitter and Weibo due to its ability to effectively process sparse, imbalanced data and identify emergent topics.

Overview of Word Network Topic Model for Short and Imbalanced Texts

The paper introduces the Word Network Topic Model (WNTM), a novel approach designed to address the challenges associated with topic modeling on short and imbalanced texts. These texts, common in online social media platforms, are characterized by extreme sparsity and distribution imbalance, posing difficulties for traditional topic modeling frameworks like LDA. WNTM leverages a word co-occurrence network-based model to simultaneously handle these issues, offering a generalizable solution across various applications.

Key Contributions and Methodology

WNTM differs significantly from previous models by focusing on the distribution of topics over individual words rather than modeling topics for each document. This shift enhances semantic density without adding significant computational overhead. By employing standard Gibbs sampling akin to LDA, WNTM retains the flexibility to be extrapolated into diverse contexts.

Key aspects of WNTM's methodology include:

1. Word Co-occurrence Network: The model constructs a network where nodes are words and edges represent their co-occurrence within a defined context, typically a sliding window. This maintains rich contextual relations while reducing sparsity.
2. Topic Assignment: Rather than relying on document-level co-occurrence, WNTM attributes topics to words based on their associations within the network, which mitigates the skewed distribution common in rare topics.
3. Simplification: The model uses Gibbs sampling, a well-established technique in topic modeling, ensuring that WNTM remains computationally feasible across different types of text data.

Experimental Findings

Experiments conducted using microblogs and standard text corpora demonstrate that WNTM surpasses LDA and other baseline methods in several performance metrics:

• Topic Coherence: WNTM produced more coherent topics, especially in sparse datasets like microblogs, affording statistically significant improvements.
• Semantic Similarity and Document Categorization: This model offered superior semantic representation, evidenced in word similarity tasks and document classification, reflecting its robustness in handling sparse data.
• Handling Imbalanced Texts: WNTM was notably better at detecting rare topics in imbalanced data, suggesting its potential in early-stage detection of emergent topics or events in social media.

Implications and Future Directions

WNTM holds significant promise for applications in real-time data analysis on platforms like Twitter and Weibo, where fast identification of trends and novel topics is crucial. Its ability to process sparse and imbalanced texts effectively makes it a versatile tool in content analysis, recommendation systems, and public opinion tracking.

Future directions might explore optimization, such as refining the word network construction process or integrating semantic distance measures to enhance topic quality further. Additionally, testing with alternative contexts for word co-occurrence may provide deeper insights into improving topic coherence across diverse datasets. Overall, WNTM represents a significant step forward in the field of topic modeling, providing a robust framework adaptable to the evolving landscape of digital text data.