Hybrid Improved Document-level Embedding (HIDE)
In recent times, word embeddings are taking a significant role in sentiment analysis. As the generation of word embeddings needs huge corpora, many applications use pretrained embeddings. In spite of the success, word embeddings suffers from certain drawbacks such as it does not capture sentiment information of a word, contextual information in terms of parts of speech tags and domain-specific information. In this work we propose HIDE a Hybrid Improved Document level Embedding which incorporates domain information, parts of speech information and sentiment information into existing word embeddings such as GloVe and Word2Vec. It combine improved word embeddings into document level embeddings. Further, Latent Semantic Analysis (LSA) has been used to represent documents as a vectors. HIDE is generated, combining LSA and document level embeddings, which is computed from improved word embeddings. We test HIDE with six different datasets and shown considerable improvement over the accuracy of existing pretrained word vectors such as GloVe and Word2Vec. We further compare our work with two existing document level sentiment analysis approaches. HIDE performs better than existing systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Sentiment AnalysisWord EmbeddingsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Hierarchical Document Encoder for Parallel Corpus Mining
We explore using multilingual document embeddings for nearest neighbor mining of parallel data. Three document-level representations are investigated: (i) document embeddings generated by simply averaging multilingual se…
Parallel Corpus MiningSentenceSentence EmbeddingSentence-Embedding+1Hybrid Losses for Hierarchical Embedding Learning
In traditional supervised learning, the cross-entropy loss treats all incorrect predictions equally, ignoring the relevance or proximity of wrong labels to the correct answer. By leveraging a tree hierarchy for fine-grai…
Multi-Task LearningRetrievalTripletHybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat model
Semantic search creates an asymmetric disclosure problem: query embeddings may reveal user intent, while returning exact provider vectors distributes reusable representations. We evaluate a deliberately restricted hybrid…
NextLevelBERT: Masked Language Modeling with Higher-Level Representations for Long Documents
While (large) language models have significantly improved over the last years, they still struggle to sensibly process long sequences found, e.g., in books, due to the quadratic scaling of the underlying attention mechan…
Document ClassificationLanguage ModelingLanguage ModellingMasked Language Modeling+3Sentiment Classification Using Document Embeddings Trained with Cosine Similarity
In document-level sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space. This paper pro…
ClassificationDocument EmbeddingGeneral ClassificationSentiment Analysis+1