Like It or Not

By Anastasia Giahanou, Fabio A. Crestani. ACM Computing Surveys, 2016

354 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6a93f293cf72c2c4c330926eb72efb4ffbf326d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

DeepHate: Hate Speech Detection via Multi-Faceted Text Representations (2020, neutral): “Tweets often contain abundant sentiment information [18].”
The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, background): “The 280-character length limitation of Tweets make them extremely noisy data and lead to sentence- or phrase level analysis to be the most suitable level of granularity (Giachanou and Crestani, 2016).”
Sentiment analysis using deep learning architectures: a review (2019, background): “…(2018), Do et al. (2019) and Giachanou and Crestani (2016) Shown great performance in Twitter domain which contains informal context and emoticons (Giachanou and Crestani 2016) Improved the performance for aspect-based sentiment analysis (Do et al. 2019) Not able to process long text due to…”
Multitask Learning for Fine-Grained Twitter Sentiment Analysis (2017, methodology): “Automatic classi cation of sentiment has mainly focused on categorizing tweets in either two (binary sentiment analysis) or three (ternary sentiment analysis) categories [8].”
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education (2023, background): “However, the sentiment analysis of tweets (Giachanou & Crestani, 2016), demonstrates that the positive sentiments (5”
A survey of sentiment analysis in social media (2018, background): “Giachanou et al. [39] investigated and brieﬂy described the algorithms of sentiment analysis in Twitter, in which researchers discussed tasks related to Twitter opinion retrieval, tracking sentiments over time, irony detection, emotion detection, and tweet sentiment quantiﬁcation.”
Interpretation of Semantic Tweet Representations (2017, background): “Researchers in Twitter analytics have found these representation learning models to be very effective for several critical tasks such as sentiment analysis [4], [14], semantic textual similarity computation [8], hashtag identification [9], etc.”