Sentiment Analysis Is a Big Suitcase

By Erik Cambria, Soujanya Poria, Alexander Gelbukh, Mike Thelwall. 2017

419 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Recent Trends in Deep Learning Based Natural Language Processing (2017, background): “Word embeddings have been responsible for state-of-the-art results in a wide range of NLP tasks [9, 10, 11, 12].”
Forward and Backward Knowledge Transfer for Sentiment Classification (2019, background): “, a product review) as expressing a positive or negative sentiment (Pang et al., 2008; Cambria et al., 2017).”
Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study (2020, neutral): “…tackle more in depth long standing questions such as understanding, measuring and monitoring the sentiment of the users towards certain topics or events Cambria, Poria, Gelbukh, and Thelwall (2017), expressed in mere texts or also by relying on other visual and vocal modalities Poria et al. (2018).”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, background): “[8] present sentiment analysis as a big suitcase of problem-solving tasks like semantic, open syntactic, and pragmatic.”
SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings (2018, methodology): “In this work, we propose an ensemble of symbolic and sub-symbolic AI techniques to perform sentiment analysis, a NLP problem that has raised growing interest within both the scientific community, for the many exciting open challenges, as well as the business world, due to the remarkable benefits to ”
Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling (2018, background): “While most works approach it as a simple categorization problem, sentiment analysis is actually a suitcase research problem [16] that requires tackling many NLP tasks, including word polarity disambiguation [17], subjectivity detection [18], personality recognition [19], microtext normalization [20]”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “key un- and under-explored problems relevant to this ﬁeld of research. Liu (2015) presents sentiment analysis as mini-NLP, given its reliance on topics covering almost the entirety of NLP. Similarly, Cambria et al. (2017) characterize sentiment analysis as a big suitcase of subtasks and subproblems,”
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM (2018, background): “In recent years, sentiment analysis (Cambria et al. 2017a) has become increasingly popular for processing social media data on online communities, blogs, wikis, microblogging platforms, and other online collaborative media.”
Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “Word embeddings have been responsible for state-ofthe-art results in a wide range of NLP tasks [9]–[12].”
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review (2019, methodology): “However, a drawback is reliance on grammatical accuracy of the sentence and the requirement for manipulation (Poria, Cambria et al., 2016). Topic modelling has been widely used to perform ACD tasks, with the most popular model being Latent Dirichlet Allocation (LDA) as implemented in Poria et al. (2”
Explainable Sentiment Analysis with Applications in Medicine (2018, background): “In terms of tasks, sentiment analysis can be considered, as a big suitcase of NLP problems [3].”
Recent trends in deep learning based personality detection (2019, background): “– Word polarity detection Personality detection is a subtask of sentiment analysis (Cambria et al. 2017) as it can be exploited for word polarity disambiguation in sentiment lexica (where the same concept can convey different emotions to different types of people) and for disambiguation between…”
SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis (2020, methodology): “If we did that, however, we should have also applied many other NLP tasks required for proper polarity detection [9], e.”
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (2018, background): “Emotion detection from such resources can beneﬁt numerous ﬁelds like counseling (De Choudhury et al., 2013), public opinion mining (Cambria et al., 2017), ﬁnancial forecasting (Xing et al., 2018), and intelligent systems such as smart homes and chat-bots (Young et al., 2018).”
Natural language based financial forecasting: a survey (2017, background): “Sentiment analysis [16] is a ‘suitcase research problem’ [19] that requires tackling many NLP sub-tasks, including aspect extraction [92], subjectivity detection [27], named entity recognition [73], and sarcasm detection [93], but also complementary tasks such as personality recognition [74], user p”