1,251 research outputs found
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
Target-based sentiment analysis involves opinion target extraction and target
sentiment classification. However, most of the existing works usually studied
one of these two sub-tasks alone, which hinders their practical use. This paper
aims to solve the complete task of target-based sentiment analysis in an
end-to-end fashion, and presents a novel unified model which applies a unified
tagging scheme. Our framework involves two stacked recurrent neural networks:
The upper one predicts the unified tags to produce the final output results of
the primary target-based sentiment analysis; The lower one performs an
auxiliary target boundary prediction aiming at guiding the upper network to
improve the performance of the primary task. To explore the inter-task
dependency, we propose to explicitly model the constrained transitions from
target boundaries to target sentiment polarities. We also propose to maintain
the sentiment consistency within an opinion target via a gate mechanism which
models the relation between the features for the current word and the previous
word. We conduct extensive experiments on three benchmark datasets and our
framework achieves consistently superior results.Comment: AAAI 201
Sentiment Analysis Based on Deep Learning: A Comparative Study
The study of public opinion can provide us with valuable information. The
analysis of sentiment on social networks, such as Twitter or Facebook, has
become a powerful means of learning about the users' opinions and has a wide
range of applications. However, the efficiency and accuracy of sentiment
analysis is being hindered by the challenges encountered in natural language
processing (NLP). In recent years, it has been demonstrated that deep learning
models are a promising solution to the challenges of NLP. This paper reviews
the latest studies that have employed deep learning to solve sentiment analysis
problems, such as sentiment polarity. Models using term frequency-inverse
document frequency (TF-IDF) and word embedding have been applied to a series of
datasets. Finally, a comparative study has been conducted on the experimental
results obtained for the different models and input feature
Document-level sentiment analysis of email data
Sisi Liu investigated machine learning methods for Email document sentiment analysis. She developed a systematic framework that has been qualitatively and quantitatively proved to be effective and efficient in identifying sentiment from massive amount of Email data. Analytical results obtained from the document-level Email sentiment analysis framework are beneficial for better decision making in various business settings
Analyzing fluctuation of topics and public sentiment through social media data
Over the past decade years, Internet users were expending rapidly in the world. They form various online social networks through such Internet platforms as Twitter, Facebook and Instagram. These platforms provide a fast way that helps their users receive and disseminate information and express personal opinions in virtual space. When dealing with massive and chaotic social media data, how to accurately determine what events or concepts users are discussing is an interesting and important problem.
This dissertation work mainly consists of two parts. First, this research pays attention to mining the hidden topics and user interest trend by analyzing real-world social media activities. Topic modeling and sentiment analysis methods are proposed to classify the social media posts into different sentiment classes and then discover the trend of sentiment based on different topics over time. The presented case study focuses on COVID-19 pandemic that started in 2019. A large amount of Twitter data is collected and used to discover the vaccine-related topics during the pre- and post-vaccine emergency use period. By using the proposed framework, 11 vaccine-related trend topics are discovered. Ultimately the discovered topics can be used to improve the readability of confusing messages about vaccines on social media and provide effective results to support policymakers in making their policy their informed decisions about public health. Second, using conventional topic models cannot deal with the sparsity problem of short text. A novel topic model, named Topic Noise based-Biterm Topic Model with FastText embeddings (TN-BTMF), is proposed to deal with this problem. Word co-occurrence patterns (i.e. biterms) are dirctly generated in BTM. A scoring method based on word co-occurrence and semantic similarity is proposed to detect noise biterms. In th
Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches
Sentiment analysis (SA) is also known as opinion mining, it is the process of gathering and analyzing people's opinions about a particular service, good, or company on websites like Twitter, Facebook, Instagram, LinkedIn, and blogs, among other places. This article covers a thorough analysis of SA and its levels. This manuscript's main focus is on aspect-based SA, which helps manufacturing organizations make better decisions by examining consumers' viewpoints and opinions of their products. The many approaches and methods used in aspect-based sentiment analysis are covered in this review study (ABSA). The features associated with the aspects were manually drawn out in traditional methods, which made it a time-consuming and error-prone operation. Nevertheless, these restrictions may be overcome as artificial intelligence develops. Therefore, to increase the effectiveness of ABSA, researchers are increasingly using AI-based machine learning (ML) and deep learning (DL) techniques. Additionally, certain recently released ABSA approaches based on ML and DL are examined, contrasted, and based on this research, gaps in both methodologies are discovered. At the conclusion of this study, the difficulties that current ABSA models encounter are also emphasized, along with suggestions that can be made to improve the efficacy and precision of ABSA systems
On the Feasibility of Transfer-learning Code Smells using Deep Learning
Context: A substantial amount of work has been done to detect smells in
source code using metrics-based and heuristics-based methods. Machine learning
methods have been recently applied to detect source code smells; however, the
current practices are considered far from mature. Objective: First, explore the
feasibility of applying deep learning models to detect smells without extensive
feature engineering, just by feeding the source code in tokenized form. Second,
investigate the possibility of applying transfer-learning in the context of
deep learning models for smell detection. Method: We use existing metric-based
state-of-the-art methods for detecting three implementation smells and one
design smell in C# code. Using these results as the annotated gold standard, we
train smell detection models on three different deep learning architectures.
These architectures use Convolution Neural Networks (CNNs) of one or two
dimensions, or Recurrent Neural Networks (RNNs) as their principal hidden
layers. For the first objective of our study, we perform training and
evaluation on C# samples, whereas for the second objective, we train the models
from C# code and evaluate the models over Java code samples. We perform the
experiments with various combinations of hyper-parameters for each model.
Results: We find it feasible to detect smells using deep learning methods. Our
comparative experiments find that there is no clearly superior method between
CNN-1D and CNN-2D. We also observe that performance of the deep learning models
is smell-specific. Our transfer-learning experiments show that
transfer-learning is definitely feasible for implementation smells with
performance comparable to that of direct-learning. This work opens up a new
paradigm to detect code smells by transfer-learning especially for the
programming languages where the comprehensive code smell detection tools are
not available
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