13,211 research outputs found
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
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
Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data
In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres
Disentangled Variational Autoencoder for Emotion Recognition in Conversations
In Emotion Recognition in Conversations (ERC), the emotions of target
utterances are closely dependent on their context. Therefore, existing works
train the model to generate the response of the target utterance, which aims to
recognise emotions leveraging contextual information. However, adjacent
response generation ignores long-range dependencies and provides limited
affective information in many cases. In addition, most ERC models learn a
unified distributed representation for each utterance, which lacks
interpretability and robustness. To address these issues, we propose a
VAD-disentangled Variational AutoEncoder (VAD-VAE), which first introduces a
target utterance reconstruction task based on Variational Autoencoder, then
disentangles three affect representations Valence-Arousal-Dominance (VAD) from
the latent space. We also enhance the disentangled representations by
introducing VAD supervision signals from a sentiment lexicon and minimising the
mutual information between VAD distributions. Experiments show that VAD-VAE
outperforms the state-of-the-art model on two datasets. Further analysis proves
the effectiveness of each proposed module and the quality of disentangled VAD
representations. The code is available at
https://github.com/SteveKGYang/VAD-VAE.Comment: Accepted by IEEE Transactions on Affective Computin
Aspect Level Sentiment Analysis using Machine Learning Approach: A Comprehensive Review
Sentimental analysis is now used from product marketing specific to the detection of social behavior. Progress on Facebook, Twitter, Youtube and other microblogging and social networking sites has not only contributed to a change in social sites, but also to the way we use these sites and the way we do it. People are fundamentally changing their feelings and their points of view with the general public. In this paper a detailed study of different approaches for lexicon-based sentiment analysis are discussed. This paper also shows that efficiency of machine learning over traditional lexicon based sentiment analysis
- …