8 research outputs found
Extraction of aspects from Online Reviews Using a Convolution Neural Network
The quality of the product is measured based on the opinions gathered from product reviews expressed on a product. Opinion mining deals with extracting the features or aspects from the reviews expressed by the users. Specifically, this model uses a deep convolutional neural network with three channels of input: a semantic word embedding channel that encodes the semantic content of the word, a part of speech tagging channel for sequential labelling and domain embedding channel for domain specific embeddings which is pooled and processed with a Softmax function. This model uses three input channels for aspect extraction. Experiments are conducted on amazon review dataset. This model achieved better result
Embarrassingly Simple Unsupervised Aspect Extraction
We present a simple but effective method for aspect identification in
sentiment analysis. Our unsupervised method only requires word embeddings and a
POS tagger, and is therefore straightforward to apply to new domains and
languages. We introduce Contrastive Attention (CAt), a novel single-head
attention mechanism based on an RBF kernel, which gives a considerable boost in
performance and makes the model interpretable. Previous work relied on
syntactic features and complex neural models. We show that given the simplicity
of current benchmark datasets for aspect extraction, such complex models are
not needed. The code to reproduce the experiments reported in this paper is
available at https://github.com/clips/catComment: Accepted as ACL 2020 short pape
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection
Unsupervised aspect detection (UAD) aims at automatically extracting
interpretable aspects and identifying aspect-specific segments (such as
sentences) from online reviews. However, recent deep learning-based topic
models, specifically aspect-based autoencoder, suffer from several problems,
such as extracting noisy aspects and poorly mapping aspects discovered by
models to the aspects of interest. To tackle these challenges, in this paper,
we first propose a self-supervised contrastive learning framework and an
attention-based model equipped with a novel smooth self-attention (SSA) module
for the UAD task in order to learn better representations for aspects and
review segments. Secondly, we introduce a high-resolution selective mapping
(HRSMap) method to efficiently assign aspects discovered by the model to
aspects of interest. We also propose using a knowledge distilling technique to
further improve the aspect detection performance. Our methods outperform
several recent unsupervised and weakly supervised approaches on publicly
available benchmark user review datasets. Aspect interpretation results show
that extracted aspects are meaningful, have good coverage, and can be easily
mapped to aspects of interest. Ablation studies and attention weight
visualization also demonstrate the effectiveness of SSA and the knowledge
distilling method
Aspect extraction on user textual reviews using multi-channel convolutional neural network
Aspect extraction is a subtask of sentiment analysis that deals with identifying opinion targets in an opinionated text. Existing approaches to aspect extraction typically rely on using handcrafted features, linear and integrated network architectures. Although these methods can achieve good performances, they are time-consuming and often very complicated. In real-life systems, a simple model with competitive results is generally more effective and preferable over complicated models. In this paper, we present a multichannel convolutional neural network for aspect extraction. The model consists of a deep convolutional neural network with two input channels: a word embedding channel which aims to encode semantic information of the words and a part of speech (POS) tag embedding channel to facilitate the sequential tagging process. To get the vector representation of words, we initialized the word embedding channel and the POS channel using pretrained word2vec and one-hot-vector of POS tags, respectively. Both the word embedding and the POS embedding vectors were fed into the convolutional layer and concatenated to a one-dimensional vector, which is finally pooled and processed using a Softmax function for sequence labeling. We finally conducted a series of experiments using four different datasets. The results indicated better performance compared to the baseline models
Commonsense Knowledge in Sentiment Analysis of Ordinance Reactions for Smart Governance
Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping.
We have then enhanced the mapping technique to link multiple tweets to SCC. In order to promote transparency in government through increased public participation, we have conducted sentiment analysis of tweets in order to evaluate the opinion of the public with respect to ordinances passed in a particular region.
Our final objective is to develop a mapping algorithm in order to directly relate ordinances to tweets. In order to fulfill this objective, we have developed a mapping technique known as TOLCS (Tweets Ordinance Linkage by Commonsense and Semantics). This technique uses pragmatic aspects in Commonsense Knowledge as well as semantic aspects by domain knowledge. By reducing the sample space of big data to be processed, this method represents an efficient way to accomplish this task.
The ultimate goal of the project is to see how closely a given region is heading towards the concept of Smart City
Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities
This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development