We have developed a multimodal prototype for public speaking with real time feedback using the Microsoft Kinect. Effective speaking involves use of gesture, facial expression, posture, voice as well as the spoken word. These modalities combine to give the appearance of self-confidence in the speaker. This initial prototype detects body pose, facial expressions and voice. Visual and text feedback is displayed in real time to the user using a video panel, icon panel and text feedback panel. The user can also set and view elapsed time during their speaking performance. Real time feedback is displayed on gaze direction, body pose and gesture, vocal tonality, vocal dysfluencies and speaking rate.
{"title":"A Multimodal System for Public Speaking with Real Time Feedback","authors":"F. Dermody, Alistair Sutherland","doi":"10.1145/2818346.2823295","DOIUrl":"https://doi.org/10.1145/2818346.2823295","url":null,"abstract":"We have developed a multimodal prototype for public speaking with real time feedback using the Microsoft Kinect. Effective speaking involves use of gesture, facial expression, posture, voice as well as the spoken word. These modalities combine to give the appearance of self-confidence in the speaker. This initial prototype detects body pose, facial expressions and voice. Visual and text feedback is displayed in real time to the user using a video panel, icon panel and text feedback panel. The user can also set and view elapsed time during their speaking performance. Real time feedback is displayed on gaze direction, body pose and gesture, vocal tonality, vocal dysfluencies and speaking rate.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85853786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In everyday life, judgments people make about others are based on brief excerpts of interactions, known as thin slices. Inferences stemming from such minimal information can be quite accurate, and nonverbal behavior plays an important role in the impression formation. Because protagonists are strangers, employment interviews are a case where both nonverbal behavior and thin slices can be predictive of outcomes. In this work, we analyze the predictive validity of thin slices of real job interviews, where slices are defined by the sequence of questions in a structured interview format. We approach this problem from an audio-visual, dyadic, and nonverbal perspective, where sensing, cue extraction, and inference are automated. Our study shows that although nonverbal behavioral cues extracted from thin slices were not as predictive as when extracted from the full interaction, they were still predictive of hirability impressions with $R^2$ values up to $0.34$, which was comparable to the predictive validity of human observers on thin slices. Applicant audio cues were found to yield the most accurate results.
{"title":"I Would Hire You in a Minute: Thin Slices of Nonverbal Behavior in Job Interviews","authors":"L. Nguyen, D. Gática-Pérez","doi":"10.1145/2818346.2820760","DOIUrl":"https://doi.org/10.1145/2818346.2820760","url":null,"abstract":"In everyday life, judgments people make about others are based on brief excerpts of interactions, known as thin slices. Inferences stemming from such minimal information can be quite accurate, and nonverbal behavior plays an important role in the impression formation. Because protagonists are strangers, employment interviews are a case where both nonverbal behavior and thin slices can be predictive of outcomes. In this work, we analyze the predictive validity of thin slices of real job interviews, where slices are defined by the sequence of questions in a structured interview format. We approach this problem from an audio-visual, dyadic, and nonverbal perspective, where sensing, cue extraction, and inference are automated. Our study shows that although nonverbal behavioral cues extracted from thin slices were not as predictive as when extracted from the full interaction, they were still predictive of hirability impressions with $R^2$ values up to $0.34$, which was comparable to the predictive validity of human observers on thin slices. Applicant audio cues were found to yield the most accurate results.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90852149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Doctoral Consortium","authors":"C. Busso","doi":"10.1145/3252454","DOIUrl":"https://doi.org/10.1145/3252454","url":null,"abstract":"","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89199725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nigel Bosch, Huili Chen, S. D’Mello, R. Baker, V. Shute
This paper discusses multimodal affect detection from a fusion of facial expressions and interaction features derived from students' interactions with an educational game in the noisy real-world context of a computer-enabled classroom. Log data of students' interactions with the game and face videos from 133 students were recorded in a computer-enabled classroom over a two day period. Human observers live annotated learning-centered affective states such as engagement, confusion, and frustration. The face-only detectors were more accurate than interaction-only detectors. Multimodal affect detectors did not show any substantial improvement in accuracy over the face-only detectors. However, the face-only detectors were only applicable to 65% of the cases due to face registration errors caused by excessive movement, occlusion, poor lighting, and other factors. Multimodal fusion techniques were able to improve the applicability of detectors to 98% of cases without sacrificing classification accuracy. Balancing the accuracy vs. applicability tradeoff appears to be an important feature of multimodal affect detection.
{"title":"Accuracy vs. Availability Heuristic in Multimodal Affect Detection in the Wild","authors":"Nigel Bosch, Huili Chen, S. D’Mello, R. Baker, V. Shute","doi":"10.1145/2818346.2820739","DOIUrl":"https://doi.org/10.1145/2818346.2820739","url":null,"abstract":"This paper discusses multimodal affect detection from a fusion of facial expressions and interaction features derived from students' interactions with an educational game in the noisy real-world context of a computer-enabled classroom. Log data of students' interactions with the game and face videos from 133 students were recorded in a computer-enabled classroom over a two day period. Human observers live annotated learning-centered affective states such as engagement, confusion, and frustration. The face-only detectors were more accurate than interaction-only detectors. Multimodal affect detectors did not show any substantial improvement in accuracy over the face-only detectors. However, the face-only detectors were only applicable to 65% of the cases due to face registration errors caused by excessive movement, occlusion, poor lighting, and other factors. Multimodal fusion techniques were able to improve the applicability of detectors to 98% of cases without sacrificing classification accuracy. Balancing the accuracy vs. applicability tradeoff appears to be an important feature of multimodal affect detection.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88503240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Catharine Oertel, Kenneth Alberto Funes Mora, Joakim Gustafson, J. Odobez
Estimating a silent participant's degree of engagement and his role within a group discussion can be challenging, as there are no speech related cues available at the given time. Having this information available, however, can provide important insights into the dynamics of the group as a whole. In this paper, we study the classification of listeners into several categories (attentive listener, side participant and bystander). We devised a thin-sliced perception test where subjects were asked to assess listener roles and engagement levels in 15-second video-clips taken from a corpus of group interviews. Results show that humans are usually able to assess silent participant roles. Using the annotation to identify from a set of multimodal low-level features, such as past speaking activity, backchannels (both visual and verbal), as well as gaze patterns, we could identify the features which are able to distinguish between different listener categories. Moreover, the results show that many of the audio-visual effects observed on listeners in dyadic interactions, also hold for multi-party interactions. A preliminary classifier achieves an accuracy of 64 %.
{"title":"Deciphering the Silent Participant: On the Use of Audio-Visual Cues for the Classification of Listener Categories in Group Discussions","authors":"Catharine Oertel, Kenneth Alberto Funes Mora, Joakim Gustafson, J. Odobez","doi":"10.1145/2818346.2820759","DOIUrl":"https://doi.org/10.1145/2818346.2820759","url":null,"abstract":"Estimating a silent participant's degree of engagement and his role within a group discussion can be challenging, as there are no speech related cues available at the given time. Having this information available, however, can provide important insights into the dynamics of the group as a whole. In this paper, we study the classification of listeners into several categories (attentive listener, side participant and bystander). We devised a thin-sliced perception test where subjects were asked to assess listener roles and engagement levels in 15-second video-clips taken from a corpus of group interviews. Results show that humans are usually able to assess silent participant roles. Using the annotation to identify from a set of multimodal low-level features, such as past speaking activity, backchannels (both visual and verbal), as well as gaze patterns, we could identify the features which are able to distinguish between different listener categories. Moreover, the results show that many of the audio-visual effects observed on listeners in dyadic interactions, also hold for multi-party interactions. A preliminary classifier achieves an accuracy of 64 %.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86895940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Techniques that use nonverbal behaviors to predict turn-taking situations, such as who will be the next speaker and the next utterance timing in multi-party meetings are receiving a lot of attention recently. It has long been known that gaze is a physical behavior that plays an important role in transferring the speaking turn between humans. Recently, a line of research has focused on the relationship between turn-taking and respiration, a biological signal that conveys information about the intention or preliminary action to start to speak. It has been demonstrated that respiration and gaze behavior separately have the potential to allow predicting the next speaker and the next utterance timing in multi-party meetings. As a multimodal fusion to create models for predicting the next speaker in multi-party meetings, we integrated respiration and gaze behavior, which were extracted from different modalities and are completely different in quality, and implemented a model uses information about them to predict the next speaker at the end of an utterance. The model has a two-step processing. The first is to predict whether turn-keeping or turn-taking happens; the second is to predict the next speaker in turn-taking. We constructed prediction models with either respiration or gaze behavior and with both respiration and gaze behaviors as features and compared their performance. The results suggest that the model with both respiration and gaze behaviors performs better than the one using only respiration or gaze behavior. It is revealed that multimodal fusion using respiration and gaze behavior is effective for predicting the next speaker in multi-party meetings. It was found that gaze behavior is more useful for predicting turn-keeping/turn-taking than respiration and that respiration is more useful for predicting the next speaker in turn-taking.
{"title":"Multimodal Fusion using Respiration and Gaze for Predicting Next Speaker in Multi-Party Meetings","authors":"Ryo Ishii, Shiro Kumano, K. Otsuka","doi":"10.1145/2818346.2820755","DOIUrl":"https://doi.org/10.1145/2818346.2820755","url":null,"abstract":"Techniques that use nonverbal behaviors to predict turn-taking situations, such as who will be the next speaker and the next utterance timing in multi-party meetings are receiving a lot of attention recently. It has long been known that gaze is a physical behavior that plays an important role in transferring the speaking turn between humans. Recently, a line of research has focused on the relationship between turn-taking and respiration, a biological signal that conveys information about the intention or preliminary action to start to speak. It has been demonstrated that respiration and gaze behavior separately have the potential to allow predicting the next speaker and the next utterance timing in multi-party meetings. As a multimodal fusion to create models for predicting the next speaker in multi-party meetings, we integrated respiration and gaze behavior, which were extracted from different modalities and are completely different in quality, and implemented a model uses information about them to predict the next speaker at the end of an utterance. The model has a two-step processing. The first is to predict whether turn-keeping or turn-taking happens; the second is to predict the next speaker in turn-taking. We constructed prediction models with either respiration or gaze behavior and with both respiration and gaze behaviors as features and compared their performance. The results suggest that the model with both respiration and gaze behaviors performs better than the one using only respiration or gaze behavior. It is revealed that multimodal fusion using respiration and gaze behavior is effective for predicting the next speaker in multi-party meetings. It was found that gaze behavior is more useful for predicting turn-keeping/turn-taking than respiration and that respiration is more useful for predicting the next speaker in turn-taking.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91535789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fabien Badeig, Quentin Pelorson, S. Arias, Vincent Drouard, I. D. Gebru, Xiaofei Li, Georgios D. Evangelidis, R. Horaud
One of the main applications of the humanoid robot NAO - a small robot companion - is human-robot interaction (HRI). NAO is particularly well suited for HRI applications because of its design, hardware specifications, programming capabilities, and affordable cost. Indeed, NAO can stand up, walk, wander, dance, play soccer, sit down, recognize and grasp simple objects, detect and identify people, localize sounds, understand some spoken words, engage itself in simple and goal-directed dialogs, and synthesize speech. This is made possible due to the robot's 24 degree-of-freedom articulated structure (body, legs, feet, arms, hands, head, etc.), motors, cameras, microphones, etc., as well as to its on-board computing hardware and embedded software, e.g., robot motion control. Nevertheless, the current NAO configuration has two drawbacks that restrict the complexity of interactive behaviors that could potentially be implemented. Firstly, the on-board computing resources are inherently limited, which implies that it is difficult to implement sophisticated computer vision and audio signal analysis algorithms required by advanced interactive tasks. Secondly, programming new robot functionalities currently implies the development of embedded software, which is a difficult task in its own right necessitating specialized knowledge. The vast majority of HRI practitioners may not have this kind of expertise and hence they cannot easily and quickly implement their ideas, carry out thorough experimental validations, and design proof-of-concept demonstrators. We have developed a distributed software architecture that attempts to overcome these two limitations. Broadly speaking, NAO's on-board computing resources are augmented with external computing resources. The latter is a computer platform with its CPUs, GPUs, memory, operating system, libraries, software packages, internet access, etc. This configuration enables easy and fast development in Matlab, C, C++, or Python. Moreover, it allows the user to combine on-board libraries (motion control, face detection, etc.) with external toolboxes, e.g., OpenCv.
{"title":"A Distributed Architecture for Interacting with NAO","authors":"Fabien Badeig, Quentin Pelorson, S. Arias, Vincent Drouard, I. D. Gebru, Xiaofei Li, Georgios D. Evangelidis, R. Horaud","doi":"10.1145/2818346.2823303","DOIUrl":"https://doi.org/10.1145/2818346.2823303","url":null,"abstract":"One of the main applications of the humanoid robot NAO - a small robot companion - is human-robot interaction (HRI). NAO is particularly well suited for HRI applications because of its design, hardware specifications, programming capabilities, and affordable cost. Indeed, NAO can stand up, walk, wander, dance, play soccer, sit down, recognize and grasp simple objects, detect and identify people, localize sounds, understand some spoken words, engage itself in simple and goal-directed dialogs, and synthesize speech. This is made possible due to the robot's 24 degree-of-freedom articulated structure (body, legs, feet, arms, hands, head, etc.), motors, cameras, microphones, etc., as well as to its on-board computing hardware and embedded software, e.g., robot motion control. Nevertheless, the current NAO configuration has two drawbacks that restrict the complexity of interactive behaviors that could potentially be implemented. Firstly, the on-board computing resources are inherently limited, which implies that it is difficult to implement sophisticated computer vision and audio signal analysis algorithms required by advanced interactive tasks. Secondly, programming new robot functionalities currently implies the development of embedded software, which is a difficult task in its own right necessitating specialized knowledge. The vast majority of HRI practitioners may not have this kind of expertise and hence they cannot easily and quickly implement their ideas, carry out thorough experimental validations, and design proof-of-concept demonstrators. We have developed a distributed software architecture that attempts to overcome these two limitations. Broadly speaking, NAO's on-board computing resources are augmented with external computing resources. The latter is a computer platform with its CPUs, GPUs, memory, operating system, libraries, software packages, internet access, etc. This configuration enables easy and fast development in Matlab, C, C++, or Python. Moreover, it allows the user to combine on-board libraries (motion control, face detection, etc.) with external toolboxes, e.g., OpenCv.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76388108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce the task of automatically classifying politically persuasive web videos and propose a highly effective multi-modal approach for this task. We extract audio, visual, and textual features that attempt to capture affect and semantics in the audio-visual content and sentiment in the viewers' comments. We demonstrate that each of the feature modalities can be used to classify politically persuasive content, and that fusing them leads to the best performance. We also perform experiments to examine human accuracy and inter-coder reliability for this task and show that our best automatic classifier slightly outperforms average human performance. Finally we show that politically persuasive videos generate more strongly negative viewer comments than non-persuasive videos and analyze how affective content can be used to predict viewer reactions.
{"title":"Exploiting Multimodal Affect and Semantics to Identify Politically Persuasive Web Videos","authors":"Behjat Siddiquie, Dave Chisholm, Ajay Divakaran","doi":"10.1145/2818346.2820732","DOIUrl":"https://doi.org/10.1145/2818346.2820732","url":null,"abstract":"We introduce the task of automatically classifying politically persuasive web videos and propose a highly effective multi-modal approach for this task. We extract audio, visual, and textual features that attempt to capture affect and semantics in the audio-visual content and sentiment in the viewers' comments. We demonstrate that each of the feature modalities can be used to classify politically persuasive content, and that fusing them leads to the best performance. We also perform experiments to examine human accuracy and inter-coder reliability for this task and show that our best automatic classifier slightly outperforms average human performance. Finally we show that politically persuasive videos generate more strongly negative viewer comments than non-persuasive videos and analyze how affective content can be used to predict viewer reactions.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"505 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77345738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu-Hao Wu, Jia Jia, Wai-Kim Leung, Yejun Liu, Lianhong Cai
As more and more people inquire to know their hearing level condition, audiometry is becoming increasingly important. However, traditional audiometric method requires the involvement of audiometers, which are very expensive and time consuming. In this paper, we present mobile personal hearing assessment (MPHA), a novel interactive mode for testing hearing level based on mobile devices. MPHA, 1) provides a general method to calibrate sound intensity for mobile devices to guarantee the reliability and validity of the audiometry system; 2) designs an audiometric correction algorithm for the real noisy audiometric environment. The experimental results show that MPHA is reliable and valid compared with conventional audiometric assessment.
{"title":"MPHA: A Personal Hearing Doctor Based on Mobile Devices","authors":"Yu-Hao Wu, Jia Jia, Wai-Kim Leung, Yejun Liu, Lianhong Cai","doi":"10.1145/2818346.2820753","DOIUrl":"https://doi.org/10.1145/2818346.2820753","url":null,"abstract":"As more and more people inquire to know their hearing level condition, audiometry is becoming increasingly important. However, traditional audiometric method requires the involvement of audiometers, which are very expensive and time consuming. In this paper, we present mobile personal hearing assessment (MPHA), a novel interactive mode for testing hearing level based on mobile devices. MPHA, 1) provides a general method to calibrate sound intensity for mobile devices to guarantee the reliability and validity of the audiometry system; 2) designs an audiometric correction algorithm for the real noisy audiometric environment. The experimental results show that MPHA is reliable and valid compared with conventional audiometric assessment.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79184101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There has been substantial progress in the field of text based sentiment analysis but little effort has been made to incorporate other modalities. Previous work in sentiment analysis has shown that using multimodal data yields to more accurate models of sentiment. Efforts have been made towards expressing sentiment as a spectrum of intensity rather than just positive or negative. Such models are useful not only for detection of positivity or negativity, but also giving out a score of how positive or negative a statement is. Based on the state of the art studies in sentiment analysis, prediction in terms of sentiment score is still far from accurate, even in large datasets [27]. Another challenge in sentiment analysis is dealing with small segments or micro opinions as they carry less context than large segments thus making analysis of the sentiment harder. This paper presents a Ph.D. thesis shaped towards comprehensive studies in multimodal micro-opinion sentiment intensity analysis.
{"title":"Micro-opinion Sentiment Intensity Analysis and Summarization in Online Videos","authors":"Amir Zadeh","doi":"10.1145/2818346.2823317","DOIUrl":"https://doi.org/10.1145/2818346.2823317","url":null,"abstract":"There has been substantial progress in the field of text based sentiment analysis but little effort has been made to incorporate other modalities. Previous work in sentiment analysis has shown that using multimodal data yields to more accurate models of sentiment. Efforts have been made towards expressing sentiment as a spectrum of intensity rather than just positive or negative. Such models are useful not only for detection of positivity or negativity, but also giving out a score of how positive or negative a statement is. Based on the state of the art studies in sentiment analysis, prediction in terms of sentiment score is still far from accurate, even in large datasets [27]. Another challenge in sentiment analysis is dealing with small segments or micro opinions as they carry less context than large segments thus making analysis of the sentiment harder. This paper presents a Ph.D. thesis shaped towards comprehensive studies in multimodal micro-opinion sentiment intensity analysis.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74035397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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