A Word Embedding Model For Topic Recommendation

被引：0
|
作者：
Kannan, Megala S. [1 ]
Mahalakshmi, G. S. [1 ]
Smitha, E. S. [2 ]
Sendhilkumar, S. [2 ]
机构：
[1] Anna Univ, Coll Engn Guindy, Dept Comp Sci & Engn, Chennai 600025, India
[2] Anna Univ, Coll Engn Guindy, Dept Informat Sci & Technol, Chennai 600025, India
来源：
PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT) | 2018年
关键词：
context analysis; hash-tag; LDA; tweet; topic modeling; word2vec;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The proposed work aims to recommend relevant hash-tags from social media posts and blog posts related to food, recipes and other popular domains using topic word-embeddings. These hash-tags can be used in turn to give food item/recipe as the user requires. For topic modeling a machine learning approach called LDA(Latent Dirichlet Allocation) is used. The method treats the social media posts as a corpus of words and applies a topic modeling to generate the relevant hash-tags. The hash-tags recommended are cross validated with a word embedding model developed from the Wikipedia corpus to check the accuracy of the recommendation system. This algorithm is further tested on posts that do not contain hash-tags to recommend new relevant hash-tags for the same.
引用
收藏
页码：1307 / 1311
页数：5
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