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Usage of Abstract Features in Semantic Sentiment Analysis

Mohammed Almashraee, Kia Teymourian, Dagmar Monett Díaz, Adrian Paschke
2014 Extended Semantic Web Conference  
Feature-based sentiment analysis can be realized on di↵erent types of object features.  ...  On the basis of such related sub-features our approach performs the extraction of more abstract features that are only implicitly included in the analysis text.  ...  In this paper, we propose an approach for the semantic sentiment analysis of abstract features based on the related sub-features.  ... 
dblp:conf/esws/AlmashraeeTDP14 fatcat:eigzam4wsvaj7euebq3vfb5o5u

Augmenting Weak Semantic Cognitive Maps with an "Abstractness" Dimension

Alexei V. Samsonovich, Giorgio A. Ascoli
2013 Computational Intelligence and Neuroscience  
The notion of weak semantic maps was introduced recently as distribution of representations in abstract spaces that are not derived from human judgments, psychometrics, or any other a priori information  ...  The emergent consensus on dimensional models of sentiment, appraisal, emotions, and values is on the semantics of the principal dimensions, typically interpreted as valence, arousal, and dominance.  ...  Thomas Sheehan for help with extraction of the relation data from WordNet. Part of Giorgio A.  ... 
doi:10.1155/2013/308176 pmid:23840200 pmcid:PMC3694378 fatcat:nfevr4c4obht7g4hzwogphwcq4

Natural Language Processing Empowered Mobile Computing

Tianyong Hao, Raymond Wong, Zhe He, Haoran Xie, Tak-Lam Wong, Fu Lee Wang
2018 Wireless Communications and Mobile Computing  
With the rapid growth of mobile device usage, more and more mobile contents offer a great opportunity for mining useful information.  ...  However, it still lacks deep text processing capabilities, such as abstraction, summarization, and semantic understanding to accomplish complex and information-intensive tasks.  ...  analysis of the product reviews heavily relied on the quality of sentiment lexicons.  ... 
doi:10.1155/2018/9130545 fatcat:o7tvmtuzhrf3tb2j364lale4mm

FeelsGoodMan: Inferring Semantics of Twitch Neologisms [article]

Pavel Dolin, Luc d'Hauthuille, Andrea Vattani
2021 arXiv   pre-print
First we establish a new baseline for sentiment analysis on Twitch data, outperforming the previous supervised benchmark by 7.9% points.  ...  There is virtually no information on the meaning or sentiment of emotes, and with a constant influx of new emotes and drift in their frequencies, it becomes impossible to maintain an updated manually-labeled  ...  We established the importance of emotes in sentiment analysis of Twitch data by examining the features of the baseline models, showcasing the importance of emote features.  ... 
arXiv:2108.08411v2 fatcat:b6dmi3nuhnbqvgecwoxe4xjjeq

Sentiment Analysis: A Survey of Current Research and Techniques
english

Jeevanandam Jothees waran, Dr. S. Koteeswaran
2015 International Journal of Innovative Research in Computer and Communication Engineering  
Analyzing the polarity of sentiment expressed in data is Opinion Mining (OM). It is a system that identifies and classifies opinion/sentiment as represented in electronic text.  ...  Economic and marketing researches depend heavily on accurate method to predict sentiments of opinions extracted from internet and predict online customer's preferences.  ...  SEMANTICS BASED A novel space designed feature to classify polarity and strength of relationships from biomedical abstracts was described by Swaminathan et al., [24] .  ... 
doi:10.15680/ijircce.2015.0305002 fatcat:gnt6bltl2bfvxnspyewmwlzqmu

Sentence Modeling with Deep Neural Architecture using Lexicon and Character Attention Mechanism for Sentiment Classification

Huy-Thanh Nguyen, Minh-Le Nguyen
2017 International Joint Conference on Natural Language Processing  
Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.  ...  Tweet-level sentiment classification in Twitter social networking has many challenges: exploiting syntax, semantic, sentiment and context in tweets.  ...  In addition, the usage of DeepCNN for characters can learns a structure of words in higher abstract level.  ... 
dblp:conf/ijcnlp/NguyenN17 fatcat:65duaogigndj3ek4foalca6ojq

Sentence Compression for Target-Polarity Word Collocation Extraction

Yanyan Zhao, Wanxiang Che, Honglei Guo, Bing Qin, Zhong Su, Ting Liu
2014 International Conference on Computational Linguistics  
We apply a discriminative conditional random field model, with some special sentimentrelated features, in order to automatically compress sentiment sentences.  ...  Target-polarity word (T-P) collocation extraction, a basic sentiment analysis task, relies primarily on syntactic features to identify the relationships between targets and polarity words.  ...  This work was supported by National Natural Science Foundation of China (NSFC) via grant 61300113, 61133012 and 61273321, the Ministry of Education Research of Social Sciences Youth funded projects via  ... 
dblp:conf/coling/ZhaoCGQSL14 fatcat:yjn3nunczvgt7opfitkrnk756e

TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

Dr. C. Arunabala, P. Jwalitha, Soniya Nuthalapati
2019 Zenodo  
In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed.  ...  The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis  ...  Deep neural network uses semantic synthesis of high-level text sentiment semantic feature vector, so as to get high-level emotional semantic expression of text, effectively improve the generalization ability  ... 
doi:10.5281/zenodo.3262182 fatcat:kjl657vmf5fx7hwhj3ve3dveaq

TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

Dr. C. Arunabala, P. Jwalitha, Soniya Nuthalapati
2019 International journal of research - granthaalayah  
In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed.  ...  The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis  ...  Deep neural network uses semantic synthesis of high-level text sentiment semantic feature vector, so as to get high-level emotional semantic expression of text, effectively improve the generalization ability  ... 
doi:10.29121/granthaalayah.v7.i6.2019.761 fatcat:hefcomkfmzblln57vu2cktwzu4

A Deep Neural Architecture for Sentence-Level Sentiment Classification in Twitter Social Networking [chapter]

Huy Nguyen, Minh-Le Nguyen
2018 Communications in Computer and Information Science  
Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.  ...  We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding.  ...  Preprocessing We firstly take unique properties of Twitter in order to reduce the feature space such as Username, Usage of links, None, URLs and Repeated Letters.  ... 
doi:10.1007/978-981-10-8438-6_2 fatcat:owpzn45dh5fgtlncpsmxviu2ku

Extractive Text Summarization for Social News using Hybrid Techniques in Opinion Mining

2020 International Journal of Engineering and Advanced Technology  
The existing work recommends a technique of hybrid text summarization that's a blend of CRF (conditional random fields) and LSA (Latent Semantic Analysis) which being highly adhesive with low redundant  ...  The technique of LSA extracts hidden semantic structures within words/sentences that being commonly utilized in the process of summarization.  ...  Latent Semantic Analysis The technique of LSA (Latent Semantic Analysis)extracts hidden semantic structures within words/sentences which being commonly utilized in the process of text summarization.  ... 
doi:10.35940/ijeat.b3356.029320 fatcat:7vwnlgsef5arpllozo24oraotu

Lexical-semantic resources: yet powerful resources for automatic personality classification [article]

Xuan-Son Vu, Lucie Flekova, Lili Jiang, Iryna Gurevych
2017 arXiv   pre-print
While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored.  ...  In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification  ...  and sentiment features in APC. • Proposing and evaluating a feature selection method called Selective.WSD to improve WSD usage in APC. • Proposing a unified framework on top of the UIMA framework 2 to  ... 
arXiv:1711.09824v1 fatcat:ir3uu2y3xjbh5m7oyyaenteqcu

Sentiment Analysis Based on Chinese Thinking Modes [chapter]

Liang Yang, Hongfei Lin, Yuan Lin
2012 Communications in Computer and Information Science  
In order to solve the implicit Chinese sentiment expression, Latent Semantic Analysis (LSA) is applied when the CSE model could not classify the implicit emotions accurately.  ...  By comparing with two traditional sentiment analysis methods, experimental results show that the performance of sentiment analysis included the Chinese thinking mode factors is significantly better than  ...  "Scatter view" on sentiment analysis.  ... 
doi:10.1007/978-3-642-34456-5_5 fatcat:g5qn43rlsfeyrpvwetspc3sv3y

LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally

Cynthia Van Hee, Els Lefever, Veronique Hoste
2015 Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)  
This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter.  ...  We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor.  ...  In what relates to the detection of metaphors, Turney et al. (2011) introduced an algorithm for distinguishing between metaphorical and literal word usages based on the degree of abstractness of a word's  ... 
doi:10.18653/v1/s15-2115 dblp:conf/semeval/HeeLH15 fatcat:lqkz4b25ung23d4fjoa5noiiru

Distinguishing Literal and Non-Literal Usage of German Particle Verbs

Maximilian Köper, Sabine Schulte im Walde
2016 Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  
In addition, PV-specific classification experiments demonstrate the role of shared particle semantics and semantically related base verbs in PV meaning shifts.  ...  This paper provides a binary, token-based classification of German particle verbs (PVs) into literal vs. non-literal usage.  ...  , and sentiment analysis.  ... 
doi:10.18653/v1/n16-1039 dblp:conf/naacl/KoperW16 fatcat:sqjvfzneibfppepv5qzv7nlm3a
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