scholarly journals Rule-based and Lightly Supervised Methods to Predict Emotions in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8953 ◽  
Author(s):  
Ted Pedersen
Keyword(s):  
Sentiment Analysis ◽  
Corpus Analysis ◽  
Measures Of Association ◽  
Rule Based ◽  
The Third ◽  
Suicide Notes ◽  
Supervised Methods ◽  
Rule Based Approach ◽  
F Measure

This paper describes the Duluth systems that participated in the Sentiment Analysis track of the i2b2/VA/Cincinnati Children's 2011 Challenge. The top Duluth system was a rule-based approach derived through manual corpus analysis and the use of measures of association to identify significant ngrams. This performed in the median range of systems, attaining an F-measure of 0.45. The second system was automatically derived from the most frequent bigrams unique to one or two emotions. It achieved an F-measure of 0.36. The third system was the union of the first two, and reached an F-measure of 0.44.

scholarly journals Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts

2020 ◽  
Author(s):  
Isanka Rajapaksha ◽  
Chanika Ruchini Mudalige ◽  
Dilini Karunarathna ◽  
Nisansa de Silva ◽  
Gathika Rathnayaka ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Rule Based ◽  
Rule Based Approach ◽  
Legal Opinion

scholarly journals Statistical and Similarity Methods for Classifying Emotion in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8958 ◽  
Author(s):  
Kirk Roberts ◽  
Sanda M. Harabagiu
Keyword(s):  
Sentiment Analysis ◽  
Similarity Measures ◽  
Classification Problem ◽  
Shared Task ◽  
Suicide Notes ◽  
F Measure

In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.

Sentiment analysis from travellers’ reviews using enhanced conjunction rule based approach for feature-specific evaluation of hotels

2020 ◽  
Vol 23 (6) ◽  
pp. 983-997
Author(s):  
Aranyak Maity ◽  
Sritama Ghosh ◽  
Saikat Karfa ◽  
Moutan Mukhopadhyay ◽  
Saurabh Pal ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Rule Based ◽  
Rule Based Approach ◽  
Specific Evaluation

scholarly journals A Hybrid System for Emotion Extraction from Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8981 ◽  
Author(s):  
Azadeh Nikfarjam ◽  
Ehsan Emadzadeh ◽  
Graciela Gonzalez
Keyword(s):  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Semantic Features ◽  
Rule Based ◽  
New Approach ◽  
Suicide Notes ◽  
Learning Techniques ◽  
Svm Model ◽  
Novel Method ◽  
F Measure

The reasons that drive someone to commit suicide are complex and their study has attracted the attention of scientists in different domains. Analyzing this phenomenon could significantly improve the preventive efforts. In this paper we present a method for sentiment analysis of suicide notes submitted to the i2b2/VA/Cincinnati Shared Task 2011. In this task the sentences of 900 suicide notes were labeled with the possible emotions that they reflect. In order to label the sentence with emotions, we propose a hybrid approach which utilizes both rule based and machine learning techniques. To solve the multi class problem a rule-based engine and an SVM model is used for each category. A set of syntactic and semantic features are selected for each sentence to build the rules and train the classifier. The rules are generated manually based on a set of lexical and emotional clues. We propose a new approach to extract the sentence's clauses and constitutive grammatical elements and to use them in syntactic and semantic feature generation. The method utilizes a novel method to measure the polarity of the sentence based on the extracted grammatical elements, reaching precision of 41.79 with recall of 55.03 for an f-measure of 47.50. The overall mean f-measure of all submissions was 48.75% with a standard deviation of 7%.

scholarly journals A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paramita Ray ◽  
Amlan Chakrabarti
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Learning Method ◽  
Rule Based ◽  
Aspect Extraction ◽  
Art Methods ◽  
Rule Based Approach

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

scholarly journals Recognition and Effective Handling of Negations in Enhancing the Accuracy of Urdu Sentiment Analyzer

2020 ◽  
Vol 39 (4) ◽  
pp. 759-771
Author(s):  
Neelam Mukhtar ◽  
Mohammad Abid Khan ◽  
Nadia Chiragh ◽  
Asim Ullah Jan ◽  
Shah Nazir
Keyword(s):  
Sentiment Analysis ◽  
Negative Word ◽  
Long Distance ◽  
Rule Based ◽  
Sentiment Lexicon ◽  
Effective Role ◽  
Rule Based Approach ◽  
Implicit And Explicit ◽  
Multiple Domains

Although work has been done in Urdu Sentiment Analysis by researchers but still there is a lot of room for improvement in the form of achieving higher accuracy. Therefore, in this research, the accuracy of Urdu Sentiment Analysis in multiple domains is enhanced by dealing negations using Lexicon-based approach, one of the broadly used approaches for performing Sentiment Analysis. Negations in Urdu Sentiment Analysis are particularly focused in this research because of their effective role in Sentiment Analysis. Both local and long distance negations are considered. For achieving this goal, a corpus with 6025 Urdu sentences, from 151 blogs that belong to 14 different genres is taken in which use of negations is carefully observed. Two major steps are taken in this regard. First, to deal with the morphological negations, this type of negations is included in the negative word file of the Urdu Sentiment Lexicon developed for performing Sentiment Analysis of Urdu blogs. Secondly, rule-based approach is used for handling the implicit and explicit negations. Rules are designed that can deal with both implicit and explicit negations effectively. Implementation of these rules increased the accuracy of Sentiment Analyzer from 73.88% to 78.32% with 0.745, 0.788 and 0.745 Precision, Recall and Fmeasure respectively, which is statistically significant improvement.

Location Named-Entity Recognition using Rule-Based Approach for Balinese Texts

2021 ◽  
Vol 9 (3) ◽  
pp. 435
Author(s):  
Ni Putu Ayu Sherly Anggita S ◽  
Ngurah Agus Sanjaya ER
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Main Task ◽  
Rule Based ◽  
Personal Names ◽  
Named Entity ◽  
Proposed Model ◽  
Rule Based Approach ◽  
F Measure

In Natural Language Processing (NLP), Named Recognition Entity (NER) is a sub-discussion widely used for research. The NER’s main task is to help identify and detect the entity-named in the sentence, such as personal names, locations, organizations, and many other entities. In this paper, we present a Location NER system for Balinese texts using a rule-based approach. NER in the Balinese document is an essential and challenging task because there is no research on this. The rule-based approach using human-made rules to extract entity name is one of the most famous ways to extract entity names as well as machine learning. The system aims to identify proper names in the corpus and classify them into locations class. Precision, recall, and F-measure used for the evaluation. Our results show that our proposed model is trustworthy enough, having average recall, precision, and f-measure values for the specific location entity, respectively, 0.935, 0.936, and 0.92. These results prove that our system is capable of recognizing named-entities of Balinese texts.

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