Results 1 - 10
of
25
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
"... Document level sentiment classification remains a challenge: encoding the intrin-sic relations between sentences in the se-mantic meaning of a document. To ad-dress this, we introduce a neural network model to learn vector-based document rep-resentation in a unified, bottom-up fash-ion. The model fi ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
(Show Context)
Document level sentiment classification remains a challenge: encoding the intrin-sic relations between sentences in the se-mantic meaning of a document. To ad-dress this, we introduce a neural network model to learn vector-based document rep-resentation in a unified, bottom-up fash-ion. The model first learns sentence rep-resentation with convolutional neural net-work or long short-term memory. After-wards, semantics of sentences and their relations are adaptively encoded in docu-ment representation with gated recurren-t neural network. We conduct documen-t level sentiment classification on four large-scale review datasets from IMDB and Yelp Dataset Challenge. Experimen-tal results show that: (1) our neural mod-el shows superior performances over sev-eral state-of-the-art algorithms; (2) gat-ed recurrent neural network dramatically outperforms standard recurrent neural net-work in document modeling for sentiment classification.1 1
OpinionFlow: Visual analysis of opinion diffusion on social media
- IEEE Transactions on Visualization and Computer Graphics
"... Abstract — It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective an ..."
Abstract
-
Cited by 3 (2 self)
- Add to MetaCart
(Show Context)
Abstract — It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we introduce a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow. Index Terms—Opinion visualization, opinion diffusion, opinion flow, influence estimation, kernel density estimation, level-of-detail. 1
Rich Lexical Features for Sentiment Analysis on Twitter
"... In this paper, we describe our system for the Sentiment Analysis of Twitter shared task in SemEval 2014. Our system uses an SVM classifier along with rich set of lexical features to detect the sentiment of a phrase within a tweet (Task-A) and also the sentiment of the whole tweet (Task-B). We start ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
In this paper, we describe our system for the Sentiment Analysis of Twitter shared task in SemEval 2014. Our system uses an SVM classifier along with rich set of lexical features to detect the sentiment of a phrase within a tweet (Task-A) and also the sentiment of the whole tweet (Task-B). We start from the lexical features that were used in the 2013 shared tasks, we en-hance the underlying lexicon and also in-troduce new features. We focus our fea-ture engineering effort mainly on Task-A. Moreover, we adapt our initial frame-work and introduce new features for Task-B. Our system reaches weighted score of 87.11 % in Task-A and 64.52 % in Task-B. This places us in the 4th rank in the Task-A and 15th in the Task-B. 1
Convolutional Neural Network Based Semantic Tagging with Entity Embeddings
"... Abstract Unsupervised word embeddings provide rich linguistic and conceptual information about words. However, they may provide weak information about domain specific semantic relations for certain tasks such as semantic parsing of natural language queries, where such information about words or phr ..."
Abstract
- Add to MetaCart
Abstract Unsupervised word embeddings provide rich linguistic and conceptual information about words. However, they may provide weak information about domain specific semantic relations for certain tasks such as semantic parsing of natural language queries, where such information about words or phrases can be valuable. To encode the prior knowledge about the semantic word relations, we extended the neural network based lexical word embedding objective function by incorporating the information about relationship between entities that we extract from knowledge bases
Samskara Minimal structural features for detecting subjectivity and polarity in Italian tweets
"... Abstract English. Sentiment analysis classification tasks strongly depend on the properties of the medium that is used to communicate opinionated content. There are some limitations in Twitter that force the user to exploit structural properties of this social network with features that have pragma ..."
Abstract
- Add to MetaCart
Abstract English. Sentiment analysis classification tasks strongly depend on the properties of the medium that is used to communicate opinionated content. There are some limitations in Twitter that force the user to exploit structural properties of this social network with features that have pragmatic and communicative functions. Samskara is a system that uses minimal structural features to classify Italian tweets as instantiations of a textual genre, obtaining good results for subjectivity classification, while polarity classification needs substantial improvements. Italiano. I compiti di classificazione a livello di sentiment analysis dipendono fortemente dalle proprietà del mezzo usato per comunicare contenuti d'opinione. Vi sono limiti oggettivi in Twitter che forzano l'utente a sfruttare le proprietà strutturali del mezzo assegnando ad alcuni elementi funzioni pragmatiche e comunicative. Samskaraè un sistema che si propone di classificare i tweets italiani come se appartenessero a un genere testuale, interprentandoli come elementi caratterizzati da strutture minimali e ottenendo buoni risultati nella classificazione della soggettività mentre la classificazione della polarità ha bisogno di sostanziali miglioramenti.
Microsummarization of Online Reviews: An Experimental Study
"... Abstract Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or microreviews, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or microreviews, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently little research on how to mine this information to present it back to users in easily consumable way. In this paper, we introduce the task of microsummarization, which combines sentiment analysis, summarization, and entity recognition in order to surface key content to users. We explore unsupervised and supervised methods for this task, and find we can reliably extract relevant entities and the sentiment targeted towards them using crowdsourced labels as supervision. In an end-to-end evaluation, we find our best-performing system is vastly preferred by judges over a traditional extractive summarization approach. This work motivates an entirely new approach to summarization, incorporating both sentiment analysis and item extraction for modernized, at-a-glance presentation of public opinion.
On the Automatic Learning of Sentiment Lexicons
"... This paper describes a simple and princi-pled approach to automatically construct sen-timent lexicons using distant supervision. We induce the sentiment association scores for the lexicon items from a model trained on a weakly supervised corpora. Our empiri-cal findings show that features extracted ..."
Abstract
- Add to MetaCart
(Show Context)
This paper describes a simple and princi-pled approach to automatically construct sen-timent lexicons using distant supervision. We induce the sentiment association scores for the lexicon items from a model trained on a weakly supervised corpora. Our empiri-cal findings show that features extracted from such a machine-learned lexicon outperform models using manual or other automatically constructed sentiment lexicons. Finally, our system achieves the state-of-the-art in Twitter Sentiment Analysis tasks from Semeval-2013 and ranks 2nd best in Semeval-2014 according to the average rank.
Using Word Embeddings for Bilingual Unsupervised WSD
"... Unsupervised Word Sense Disambigua-tion (WSD) is one of the challenging prob-lems in natural language processing. Re-cently, an unsupervised bilingual WSD ap-proach has been proposed. This approach uses context aware EM formulation for es-timating the sense distribution by using the co-occurrence co ..."
Abstract
- Add to MetaCart
(Show Context)
Unsupervised Word Sense Disambigua-tion (WSD) is one of the challenging prob-lems in natural language processing. Re-cently, an unsupervised bilingual WSD ap-proach has been proposed. This approach uses context aware EM formulation for es-timating the sense distribution by using the co-occurrence counts of cross-linked words in comparable corpora. WordNet-based similarity measures are used for ap-proximating the co-occurrence counts. In this paper, we explore the feasibility of the use of Word Embeddings for approx-imating these counts, which is an exten-sion to the existing approach. We evalu-ated our approach for Hindi-Marathi lan-guage pair, on Health domain. On us-ing the combination of Word Embeddings and WordNet-based similarity measures, we observed 8.5 % and 2.5 % improvement in the F-score of verbs and adjectives re-spectively for Marathi and 7 % improve-ment in the F-score of adjectives for Hindi. The experiments show that the combina-tion of Word Embeddings and WordNet-based similarity measures is a reasonable approximation for the bilingual WSD. 1
Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets
"... This paper describes our sentiment classifica-tion system submitted to SemEval-2015 Task 10. In the message-level polarity classifica-tion subtask, we obtain the highest macro-averaged F1-scores on three out of six test-ing sets. Specifically, we build a two-stage classifier to predict the sentiment ..."
Abstract
- Add to MetaCart
This paper describes our sentiment classifica-tion system submitted to SemEval-2015 Task 10. In the message-level polarity classifica-tion subtask, we obtain the highest macro-averaged F1-scores on three out of six test-ing sets. Specifically, we build a two-stage classifier to predict the sentiment labels for tweets, which enables us to design different features for subjective/objective classification and positive/negative classification. In addi-tion to n-grams, lexicons, word clusters, and twitter-specific features, we develop several deep learning methods to automatically ex-tract features for the message-level sentiment classification task. Moreover, we propose a polarity boosting trick which improves the performance of our system. 1
Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation
"... Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. How-ever, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Ex-amples of these approaches include an ..."
Abstract
- Add to MetaCart
Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. How-ever, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Ex-amples of these approaches include an ap-proach that uses a polarity dictionary that can-not handle unknown or newly invented words and another approach that uses a complex model with 13 types of feature templates. We propose a novel high performance sentiment classification method with stacked denoising auto-encoders that uses distributed word rep-resentation instead of building dictionaries or utilizing engineering features. The results of experiments conducted indicate that our model achieves state-of-the-art performance in Japanese sentiment classification tasks. 1
