Short text classification in twitter to improve information filtering

By B. Sriram, D. Fuhry, Engin Demir, H. Ferhatosmanoğlu, Murat Demirbas. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010

885 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/e23ac4b63031d9f445ec7c297a237b514082347c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yuan Yunhao
2. Hongwei Hao
3. Tamer Elsayed
4. Chenglin Liu
5. Aixin Sun
6. Walid Magdy
7. Li Yun
8. Haoran Wang

In Information Systems

1. Yuan Yunhao
2. Tamer Elsayed
3. Chenglin Liu
4. Aixin Sun
5. Walid Magdy
6. Li Yun
7. Zongyang Ma
8. Chenliang Li

Citations in the corpus

• Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, methodology): “Short text topic modeling algorithms are always applied into many tasks such as topic detection [4], classification [5], comment summarization [6], user interest profiling [7].”
• Query Expansion Techniques for Information Retrieval: a Survey (2017, neutral): “Other techniques include using user’s profile [284], using geographical query footprint [96], using correlated keywords [296], using the links and anchor text in Wikipedia [14], using text classification in twitter messages [250] and using the patterns of online users behavior [287] [100].”
• Semantic Clustering and Convolutional Neural Network for Short Text Categorization (2015, methodology): “Traditional statistics-based methods usually fail to achieve satisfactory performance for short texts classification due to their sparsity of representations (Sriram et al., 2010). Based on external Wikipedia corpus, Phan et al. (2008) proposed a method to discover hidden topics using LDA and”
• Topic Modeling for Short Texts with Auxiliary Word Embeddings (2016, background): “Given the large volume of short texts available, effective and efficient models to discover the latent topics from short texts become fundamental to many applications that require semantic understanding of textual content, such as user interest profiling [38], topic detection [37], comment summariza”
• Adaptive Method for Following Dynamic Topics on Twitter (2014, background): “Phuvipadawat and Murata (2010) focused on tracking and detection of breaking news using predefined search queries, e.g., #breakingnews and “breaking news”.”