A context-aware approach to detection of short irrelevant texts

Xie, Sihong; Wang, Jing; Amin, Mohammad Shafkat; Yan, Baoshi; Bhasin, Anmol; Yu, Clement; Yu, Philip S.

doi:10.1109/dsaa.2015.7344831

Cited by 4 publications

(14 citation statements)

References 34 publications

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“…They applied a combination of co-reference resolution and Wikipedia embedding to replace pronouns with corresponding nouns and used the topic modeling method LDA to group related terms in the same topics. A context-aware approach to detect irrelevant comments following posts was proposed by Xie et al in [3]. Their approach assumed that the context-aware semantics of a comment are determined by the semantic environment where the comment is located.…”

Section: Previous Workmentioning

confidence: 99%

“…For the first time, Hieu et al [22] used LDA to enhance the bag-of-word approach and thereby deal with short and sparse texts by finding most of the hidden topics similar to them from large scale data collections. Xie et al [3] proposed a framework to identify relevant and irrelevant texts by capturing the semantic of short texts in a context-aware approach. Their work considered topic similarity in short texts to capture their relevancy to each other.…”

Section: Previous Workmentioning

confidence: 99%

“…Since readers consider comments as a valid source of supplemental information, they prefer to see comments that are more meaningful and discuss a post's topics rather than unrelated concepts such as personal opinions, advertisements, bot-generated content, etc. Therefore, identifying such unrelated comments following a post is a big challenge in social media content analysis [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

“…Other studies have tried to identify unrelated comments that are generated to distribute spontaneous spam, influence public opinions, advertise products and events, etc. by leveraging on text contents or temporal and spatial user behavior in social media [3], [5], [12]. In some content analysis applications, where dealing with posts and following comments as short texts in social media, we may not have access to a post's complete story or to some external corpuses such as Wikipedia or Google web pages related to the post content to enhance existing short texts.…”

Section: Introductionmentioning

confidence: 99%

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Content Similarity Analysis of Written Comments under Posts in Social Media

Mozafari¹,

Farahbakhsh²,

Crespi³

2019

2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Content Similarity Analysis of Written Comments under Posts in Social Media

Mozafari¹,

Farahbakhsh²,

Crespi³

2019

2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)

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“…Future studies should investigate this trade-off. In addition, the analysis of textual content possibly could be improved with statistical learning techniques [1] [11] or context-aware semantics [22] rather than through a simplistic use of keywords. …”

Section: Future Researchmentioning

confidence: 99%

Automatic Detection of Irrelevant Comments in an Electronic Meeting

Aiken

Garner

2017

IJMIT

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I S S N 2 2 7 8 -5 6 1 2 V o l u m e 12 N u m b e r 1 I n t e r n a t i o n a l J o u r n a l o f M a n a g e m e n t a n d I n f o r m a t i o n T e c h n o l o g y 3123 | P a g e M ay 2 0 1 7 w w w . c i r w o r l d . c o m Automatic Detection of Irrelevant Comments in an Electronic MeetingMilam Aiken, Bart GarnerSchool of Business Administration, University of Mississippi, University, MS 38677 ABSTRACTGroups exchanging ideas in electronic meetings often generate irrelevant or off-topic comments that can detract from the conversation. Here, we describe a system that seeks to identify this immaterial text using previously identified keywords.Results of an experiment with the system show that group members believe meetings do have irrelevant comments that waste time, but participants often enjoy them. The system achieved an F measure of 42.3% for recall and precision, and further research is necessary to determine if this is sufficient or what can be done to improve this score.

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