Systematic Survey on Sentiment Analysis

Jain, Shubham; Singh, Pardeep

doi:10.1109/icsccc.2018.8703370

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Starcraft), important events may take place outside of the player's vision range which are often signalled through sound effects. These sounds can then be analysed in more detail in order to create an appropriate response: while often intuitive for humans, machines can make use of sentiment analysis research [15], for example, to identify what specific sounds might mean. Another interesting line of future work regarding audio analysis would be looking into exactly how removing certain sounds affects the agent's performance, in the context of having an agent proficient enough to be able to solve the games given enough information.…”

Section: Discussionmentioning

confidence: 99%

“Did You Hear That?” Learning to Play Video Games from Audio Cues

Gaina

Stephenson²

2019

2019 IEEE Conference on Games (CoG)

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Game-playing AI research has focused for a long time on learning to play video games from visual input or symbolic information. However, humans benefit from a wider array of sensors which we utilise in order to navigate the world around us. In particular, sounds and music are key to how many of us perceive the world and influence the decisions we make. In this paper, we present initial experiments on game-playing agents learning to play video games solely from audio cues. We expand the Video Game Description Language to allow for audio specification, and the General Video Game AI framework to provide new audio games and an API for learning agents to make use of audio observations. We analyse the games and the audio game design process, include initial results with simple Q-Learning agents, and encourage further research in this area.

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Section: Discussionmentioning

confidence: 99%

“Did You Hear That?” Learning to Play Video Games from Audio Cues

Gaina

Stephenson²

2019

2019 IEEE Conference on Games (CoG)

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“…Moderation and content filtering are, therefore, very important to integrate within our human interaction module. One form of filtering for textual and speech-based content is sentiment analysis [56], which we would use to identify possibly harmful messages received by Thyia before she gets to process them and react accordingly. However, even though research in the area of textual sentiment analysis is plentiful, it is harder to apply the same tools for game content: how could one identify if a given game is unethical?…”

Section: Ethical Implicationsmentioning

confidence: 99%

Project Thyia: A Forever Gameplayer

Gaina

Lucas

Peŕez-Liebana

2019

2019 IEEE Conference on Games (CoG)

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The space of Artificial Intelligence entities is dominated by conversational bots. Some of them fit in our pockets and we take them everywhere we go, or allow them to be a part of human homes. Siri, Alexa, they are recognised as present in our world. But a lot of games research is restricted to existing in the separate realm of software. We enter different worlds when playing games, but those worlds cease to exist once we quit. Similarly, AI game-players are run once on a game (or maybe for longer periods of time, in the case of learning algorithms which need some, still limited, period for training), and they cease to exist once the game ends. But what if they didn't? What if there existed artificial game-players that continuously played games, learned from their experiences and kept getting better? What if they interacted with the real world and us, humans: livestreaming games, chatting with viewers, accepting suggestions for strategies or games to play, forming opinions on popular game titles? In this paper, we introduce the vision behind a new project called Thyia, which focuses around creating a present, continuous, 'always-on', interactive game-player.

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“…Subjectivity analysis is also considered a tool in the analysis of social problems (Chun et al , 2020; Lee et al , 2018; Molina et al , 2019; Murnion et al , 2018), such as cyberbullying, abusive language, gender violence in texts or parental control. These analyses can be performed considering two main approaches, one of them based on machine learning (ML) and the other in the use of lexicons; nevertheless, the results are better when both strategies are combined (Jain and Singh, 2019; Kaur and Mangat, 2017; Medhat et al , 2014). Another recent study proposed recurrent neural network for sentiment analysis (RNSA), a novel deep learning-based method for sentiment classification that integrates sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity, contextual polarity, sentence types, word coverage limit of an individual lexicon and word sense variations (Abdi et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

A novel approach to the creation of a labelling lexicon for improving emotion analysis in text

Navarrete

Martínez-Araneda

Vidal‐Castro

et al. 2021

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Purpose This paper aims to describe the process used to create an emotion lexicon enriched with the emotional intensity of words and focuses on improving the emotion analysis process in texts. Design/methodology/approach The process includes setting, preparation and labelling stages. In the first stage, a lexicon is selected. It must include a translation to the target language and labelling according to Plutchik’s eight emotions. The second stage starts with the validation of the translations. Then, it is expanded with the synonyms of the emotion synsets of each word. In the labelling stage, the similarity of words is calculated and displayed using WordNet similarity. Findings The authors’ approach shows better performance to identification of the predominant emotion for the selected corpus. The most relevant is the improvement obtained in the results of the emotion analysis in a hybrid approach compared to the results obtained in a purist approach. Research limitations/implications The proposed lexicon can still be enriched by incorporating elements such as emojis, idioms and colloquial expressions. Practical implications This work is part of a research project that aids in solving problems in a digital society, such as detecting cyberbullying, abusive language and gender violence in texts or exercising parental control. Detection of depressive states in young people and children is added. Originality/value This semi-automatic process can be applied to any language to generate an emotion lexicon. This resource will be available in a software tool that implements a crowdsourcing strategy allowing the intensity to be re-labelled and new words to be automatically incorporated into the lexicon.

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Systematic Survey on Sentiment Analysis

Cited by 7 publications

References 29 publications

“Did You Hear That?” Learning to Play Video Games from Audio Cues

“Did You Hear That?” Learning to Play Video Games from Audio Cues

Project Thyia: A Forever Gameplayer

A novel approach to the creation of a labelling lexicon for improving emotion analysis in text

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