A Survey on Sentiment Lexicon Creation and Analysis

2022 ◽  
pp. 579-587
Author(s):  
Ashish R. Lahase ◽  
Mahesh Shelke ◽  
Rajkumar Jagdale ◽  
Sachin Deshmukh
Keyword(s):  
Sentiment Lexicon

Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction

2021 ◽  
Author(s):  
Felipe Bravo-Marquez ◽  
Arun Khanchandani ◽  
Bernhard Pfahringer
Keyword(s):  
Sentiment Lexicon

scholarly journals Building an Arabic Sentiment Lexicon Using Semi-supervised Learning

2014 ◽  
Vol 26 (4) ◽  
pp. 417-424 ◽  
Author(s):  
Fawaz H.H. Mahyoub ◽  
Muazzam A. Siddiqui ◽  
Mohamed Y. Dahab
Keyword(s):  
Supervised Learning ◽  
Sentiment Lexicon

scholarly journals Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

2014 ◽  
Vol 2014 ◽  
pp. 1-19
Author(s):  
Liliana Ibeth Barbosa-Santillán ◽  
Inmaculada Álvarez-de-Mon y-Rego
Keyword(s):  
Graphics Processing Units ◽  
Pearson Correlation ◽  
Lexical Entry ◽  
A Value ◽  
Sentiment Lexicon ◽  
Unit Of Measurement ◽  
Time Required ◽  
Graphics Processing ◽  
High Processing

This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral{P,N,Z}depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and −1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and −1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.

scholarly journals Corpus-Based Techniques for Sentiment Lexicon Generation: A Review

2019 ◽  
Vol 17 (5) ◽  
pp. 296
Author(s):  
Mohammad Darwich ◽  
Shahrul Azman Mohd Noah ◽  
Nazlia Omar ◽  
Nurul Aida Osman
Keyword(s):  
Sentiment Lexicon

scholarly journals Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora

2018 ◽  
Vol 6 ◽  
pp. 269-285 ◽  
Author(s):  
Andrius Mudinas ◽  
Dell Zhang ◽  
Mark Levene
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Two Phase ◽  
Transductive Learning ◽  
Domain Specific ◽  
Sentiment Lexicon ◽  
Supervised Learning Algorithms

There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words (“seeds”). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, a nd t hen u ses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier v ia supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches.

Building Chinese Sentiment Lexicon Based on HowNet

2011 ◽  
Vol 187 ◽  
pp. 405-410 ◽  
Author(s):  
Yu Wei Liu ◽  
Shi Bin Xiao ◽  
Tao Wang ◽  
Shui Cai Shi
Keyword(s):  
Semantic Similarity ◽  
Chinese Text ◽  
Sentence Structure ◽  
Orientation Analysis ◽  
Sentiment Lexicon ◽  
Basic Work ◽  
Good Classification ◽  
Semantic Orientation ◽  
Sentiment Orientation

Judging the sentiment orientation of Chinese words is the basic work of the passage sentiment orientation research. Using Chinese basic sentiment words and corpus, we can identify sentiment words in the passage and expand sentiment lexicon effectively in order to improve the result of text semantic orientation analysis. With the basis of HowNet [1] sentiment words, we construct a Chinese sentiment lexicon by analyzing sentence structure and calculating the score of semantic similarity. We conduct Chinese text sentiment orientation classification experiment with this lexicon, the result shows the accuracy has achieved above 70% and obtained quite good classification effect.

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