12,167 research outputs found
Lifelong Learning CRF for Supervised Aspect Extraction
This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.Comment: Accepted at ACL 2017. arXiv admin note: text overlap with
arXiv:1612.0794
Sentiment Analysis Based on Deep Learning: A Comparative Study
The study of public opinion can provide us with valuable information. The
analysis of sentiment on social networks, such as Twitter or Facebook, has
become a powerful means of learning about the users' opinions and has a wide
range of applications. However, the efficiency and accuracy of sentiment
analysis is being hindered by the challenges encountered in natural language
processing (NLP). In recent years, it has been demonstrated that deep learning
models are a promising solution to the challenges of NLP. This paper reviews
the latest studies that have employed deep learning to solve sentiment analysis
problems, such as sentiment polarity. Models using term frequency-inverse
document frequency (TF-IDF) and word embedding have been applied to a series of
datasets. Finally, a comparative study has been conducted on the experimental
results obtained for the different models and input feature
Dynamic Classification of Sentiments from Restaurant Reviews Using Novel Fuzzy-Encoded LSTM
User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings
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