1,904 research outputs found
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Traditional sentiment analysis approaches tackle problems like ternary
(3-category) and fine-grained (5-category) classification by learning the tasks
separately. We argue that such classification tasks are correlated and we
propose a multitask approach based on a recurrent neural network that benefits
by jointly learning them. Our study demonstrates the potential of multitask
models on this type of problems and improves the state-of-the-art results in
the fine-grained sentiment classification problem.Comment: International ACM SIGIR Conference on Research and Development in
Information Retrieval 201
Semantic Tagging with Deep Residual Networks
We propose a novel semantic tagging task, sem-tagging, tailored for the
purpose of multilingual semantic parsing, and present the first tagger using
deep residual networks (ResNets). Our tagger uses both word and character
representations and includes a novel residual bypass architecture. We evaluate
the tagset both intrinsically on the new task of semantic tagging, as well as
on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an
auxiliary loss function predicting our semantic tags, significantly outperforms
prior results on English Universal Dependencies POS tagging (95.71% accuracy on
UD v1.2 and 95.67% accuracy on UD v1.3).Comment: COLING 2016, camera ready versio
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis
This paper describes two systems that were used by the authors for addressing
Arabic Sentiment Analysis as part of SemEval-2017, task 4. The authors
participated in three Arabic related subtasks which are: Subtask A (Message
Polarity Classification), Sub-task B (Topic-Based Message Polarity
classification) and Subtask D (Tweet quantification) using the team name of
NileTMRG. For subtask A, we made use of our previously developed sentiment
analyzer which we augmented with a scored lexicon. For subtasks B and D, we
used an ensemble of three different classifiers. The first classifier was a
convolutional neural network for which we trained (word2vec) word embeddings.
The second classifier consisted of a MultiLayer Perceptron, while the third
classifier was a Logistic regression model that takes the same input as the
second classifier. Voting between the three classifiers was used to determine
the final outcome. The output from task B, was quantified to produce the
results for task D. In all three Arabic related tasks in which NileTMRG
participated, the team ranked at number one
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