Every word is valuable: Studied influence of negative words that spread during election period in social media.
Xiangyu HuLemin LiTingmin WuXiaoxiang AiJie GuSheng WenPublished in: Concurr. Comput. Pract. Exp. (2019)
Keyphrases
- social media
- n gram
- related words
- english words
- word recognition
- word sense disambiguation
- unknown words
- word frequencies
- word meaning
- word pairs
- presidential election
- word segmentation
- word co occurrence
- word similarity
- text corpus
- multiword
- chinese word segmentation
- linguistic information
- syntactic categories
- query words
- information diffusion
- positive and negative
- linguistic knowledge
- noun phrases
- lexical information
- stop words
- word level
- frequency counts
- word meanings
- co occurrence
- automatic transcription
- latent topics
- numeral strings
- spoken document retrieval
- distributional clustering
- keywords
- power law distribution
- social networks
- natural language processing
- out of vocabulary
- viral marketing
- user generated content
- punctuation marks
- information propagation
- compound words
- language model
- text classification
- online social networks
- natural language text
- text corpora
- speech recognition systems
- social media data
- chinese text
- social media sites
- short list
- lexical features
- information extraction
- word frequency