13 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
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
Target-based sentiment analysis or aspect-based sentiment analysis (ABSA)
refers to addressing various sentiment analysis tasks at a fine-grained level,
which includes but is not limited to aspect extraction, aspect sentiment
classification, and opinion extraction. There exist many solvers of the above
individual subtasks or a combination of two subtasks, and they can work
together to tell a complete story, i.e. the discussed aspect, the sentiment on
it, and the cause of the sentiment. However, no previous ABSA research tried to
provide a complete solution in one shot. In this paper, we introduce a new
subtask under ABSA, named aspect sentiment triplet extraction (ASTE).
Particularly, a solver of this task needs to extract triplets (What, How, Why)
from the inputs, which show WHAT the targeted aspects are, HOW their sentiment
polarities are and WHY they have such polarities (i.e. opinion reasons). For
instance, one triplet from "Waiters are very friendly and the pasta is simply
average" could be ('Waiters', positive, 'friendly'). We propose a two-stage
framework to address this task. The first stage predicts what, how and why in a
unified model, and then the second stage pairs up the predicted what (how) and
why from the first stage to output triplets. In the experiments, our framework
has set a benchmark performance in this novel triplet extraction task.
Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art
related methods.Comment: This paper is accepted in AAAI 202
Dynamic Classification of Sentiments from Restaurant Reviews Using Novel Fuzzy-Encoded LSTM
User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings
An Optimized Deep ConvNet Sentiment Classification Model with Word Embedding and BiLSTM Technique
Sentiment Classification is a key area of natural language processing research that is frequently utilized in several industries. The goal of sentiment analysis is to figure out if a product or service received a negative or positive response. Sentiment analysis is widely utilized in several commercial fields to enhance the quality of services (QoS) for goods or services by gaining a better knowledge of consumer feedback. Deep learning provides cutting-edge achievements in a variety of complex fields. The goal of the study is to propose an improved approach for evaluating and categorising sentiments into different groups. This study proposes a novel hybridised model that combines the benefits of deep learning technologies Dual LSTM (Long Short Term Memory) and CNN (Convolution Neural Network) with the word embedding technique. The performance of three distinct word embedding approaches is compared in order to choose the optimal embedding for the proposed model's implementation. In addition, attention-based BiLSTM is used in a multi-convolutional approach. Standard measures were used to verify the validity of the suggested model's performance. The results show that the proposed model has a significantly enhanced accuracy of 96.56%, which is significantly better than existing models
A Review of Text Corpus-Based Tourism Big Data Mining
With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years