24,271 research outputs found
Multi-lingual Opinion Mining on YouTube
In order to successfully apply opinion mining (OM) to the large amounts of user-generated content produced every day, we need robust models that can handle the noisy input well yet can easily be adapted to a new domain or language. We here focus on opinion mining for YouTube by (i) modeling classifiers that predict the type of a comment and its polarity, while distinguishing whether the polarity is directed towards the product or video; (ii) proposing a robust shallow syntactic structure (STRUCT) that adapts well when tested across domains; and (iii) evaluating the effectiveness on the proposed structure on two languages, English and Italian. We rely on tree kernels to automatically extract and learn features with better generalization power than traditionally used bag-of-word models. Our extensive empirical evaluation shows that (i) STRUCT outperforms the bag-of-words model both within the same domain (up to 2.6% and 3% of absolute improvement for Italian and English, respectively); (ii) it is particularly useful when tested across domains (up to more than 4% absolute improvement for both languages), especially when little training data is available (up to 10% absolute improvement) and (iii) the proposed structure is also effective in a lower-resource language scenario, where only less accurate linguistic processing tools are available
FVEC feature and Machine Learning Approach for Indonesian Opinion Mining on YouTube Comments
Mining opinions from Indonesian comments from YouTube videos are required to extract interesting patterns and valuable information from consumer feedback. Opinions can consist of a combination of sentiments and topics from comments. The features considered in the mining of opinion become one of the important keys to getting a quality opinion. This paper proposes to utilize FVEC and TF-IDF features to represent the comments. In addition, two popular machine learning approaches in the field of opinion mining, i.e., SVM and CNN, are explored separately to extract opinions in Indonesian comments of YouTube videos. The experimental results show that the use of FVEC features on SVM and CNN achieves a very significant effect on the quality of opinions obtained, in term of accuracy
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles
With one billion monthly viewers, and millions of users discussing and
sharing opinions, comments below YouTube videos are rich sources of data for
opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset,
a freely-available collections of more than 50,000 YouTube comments and
metadata below autonomous vehicle (AV)-related videos. We describe its creation
process, its content and data format, and discuss its possible usages.
Especially, we do a case study of the first self-driving car fatality to
evaluate the dataset, and show how we can use this dataset to better understand
public attitudes toward self-driving cars and public reactions to the accident.
Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on
Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018
Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments
Opinion mining on social media posts has become more and more popular. Users
often express their opinion on a topic not only with words but they also use
image symbols such as emoticons and emoji. In this paper, we investigate the
effect of emoji-based features in opinion classification of Uzbek texts, and
more specifically movie review comments from YouTube. Several classification
algorithms are tested, and feature ranking is performed to evaluate the
discriminative ability of the emoji-based features.Comment: 10 pages, 1 figure, 3 table
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User sentiment detection: a YouTube use case
In this paper we propose an unsupervised lexicon-based approach to detect the sentiment polarity of user comments in YouTube. Polarity detection in social media content is challenging not only because of the existing limitations in current sentiment dictionaries but also due to the informal linguistic styles used by users. Present dictionaries fail to capture the sentiments of community-created terms. To address the challenge we adopted a data-driven approach and prepared a social media specific list of terms and phrases expressing user sentiments and opinions. Experimental evaluation shows the combinatorial approach has greater potential. Finally, we discuss many research challenges involving social media sentiment analysis
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Despite the recent advances in opinion mining for written reviews, few works
have tackled the problem on other sources of reviews. In light of this issue,
we propose a multi-modal approach for mining fine-grained opinions from video
reviews that is able to determine the aspects of the item under review that are
being discussed and the sentiment orientation towards them. Our approach works
at the sentence level without the need for time annotations and uses features
derived from the audio, video and language transcriptions of its contents. We
evaluate our approach on two datasets and show that leveraging the video and
audio modalities consistently provides increased performance over text-only
baselines, providing evidence these extra modalities are key in better
understanding video reviews.Comment: Second Grand Challenge and Workshop on Multimodal Language ACL 202
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