Extracting Sentiment Networks from Shakespeare's Plays

Nalisnick, Eric; Baird, Henry S.

doi:10.1109/icdar.2013.155

Cited by 54 publications

(11 citation statements)

References 13 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The earlier research concentrates on textual data for sentiment analysis but more recently multimodal data such as textual‐acoustic‐visual modality were considered for sentiment classification and affective computing . Usually, handcrafted features or lexicons or networks or ontologies are used in sentiment classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi‐level feature optimization and multimodal contextual fusion for sentiment analysis and emotion classification

Huddar¹,

Sannakki²,

Rajpurohit³

2020

Computational Intelligence

View full text Add to dashboard Cite

The availability of the humongous amount of multimodal content on the internet, the multimodal sentiment classification, and emotion detection has become the most researched topic. The feature selection, context extraction, and multi‐modal fusion are the most important challenges in multimodal sentiment classification and affective computing. To address these challenges this paper presents multilevel feature optimization and multimodal contextual fusion technique. The evolutionary computing based feature selection models extract a subset of features from multiple modalities. The contextual information between the neighboring utterances is extracted using bidirectional long‐short‐term‐memory at multiple levels. Initially, bimodal fusion is performed by fusing a combination of two unimodal modalities at a time and finally, trimodal fusion is performed by fusing all three modalities. The result of the proposed method is demonstrated using two publically available datasets such as CMU‐MOSI for sentiment classification and IEMOCAP for affective computing. Incorporating a subset of features and contextual information, the proposed model obtains better classification accuracy than the two standard baselines by over 3% and 6% in sentiment and emotion classification, respectively.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Multi‐level feature optimization and multimodal contextual fusion for sentiment analysis and emotion classification

Huddar¹,

Sannakki²,

Rajpurohit³

2020

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…More recent studies in the field of digital humanities approach emotion analysis from various angles and for a wide range of goals. Some studies use emotions as feature input for genre classification (Samothrakis and Fasli, 2015;Henny-Krahmer, 2018;Yu, 2008;Kim et al, 2017), story clustering (Reagan et al, 2016), mapping emotions to geographical locations in literature (Heuser et al, 2016), and construction of social networks of characters (Nalisnick and Baird, 2013;Jhavar and Mirza, 2018). Other studies use emotion analysis as a starting point for stylometry (Koolen, 2018), inferring psychological characters' traits (Egloff et al, 2018), and analysis of the causes of emotions in literature (Kim andKlinger, 2018, 2019).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Second Workshop on Storytelling

Ferraro¹,

Huang²,

Lukin³

2019

View full text Add to dashboard Cite

Pictures can enrich storytelling experiences. We propose a framework that can automatically compose a picture book by understanding story text and visualizing it with painting elements, i.e., characters and backgrounds. For story understanding, we extract key information from a story on both sentence level and paragraph level, including characters, scenes and actions. These concepts are organized and visualized in a way that depicts the development of a story. We collect a set of Chinese stories for children and apply our approach to compose pictures for stories. Extensive experiments are conducted towards story event extraction for visualization to demonstrate the effectiveness of our method.

show abstract

“…The Area Under the Cumulative density curve (AUC) is then computed from the CDF (equation (12)) of the consensus matrix across a range [2,10] of possible values of k using equation (13).…”

Section: Estimation Of the Number Of Homogeneous Regionsmentioning

confidence: 99%

“…Numerous research projects, such as HisDoc 3 , DocExplore 4 , Europeana 5 , DEBORA 6 , BAMBI 7 , MADONNE 8 , NaviDoMass 9 , Passe-Partout 10 and GRAPHEM 11 are looking at the digitization of European and American ancient heritage resources. The French digital library Gallica 12 , the British library 13 and the John F. Kennedy library 14 , have been established for the purpose of preserving and exploiting this cultural heritage. For instance, the European project DEBORA aims to develop networked libraries by improving accessibility to the 16 th century books of Italy, France and Portugal [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the issues of document image analysis have been considered as texture segmentation and classification [10]. Moreover, some similarities of document content type have been deduced from many book pages [11,12]. In addition, based on the assumption that texture can characterize a document content type which is usually repeated on many pages of the same book, we propose a framework that works on entire book scale instead of processing each page individually.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A texture-based pixel labeling approach for historical books

Mehri

Gomez‐Krämer

Héroux

et al. 2015

Pattern Anal Applic

View full text Add to dashboard Cite

Over the last few years, there has been tremendous growth in the automatic processing of digitized historical documents. In fact, finding reliable systems for the interpretation of ancient documents has been a topic of major interest for many libraries and the prime issue of research in the document analysis community. One important challenge is to refine well-known approaches based on strong a priori knowledge (e.g. the document image content, layout, typography, font size and type, scanning resolution, image size, etc.). Nevertheless, a texture analysis approach has consistently been chosen to segment a page layout when information is lacking on document structure and content. Thus, in this article a framework is proposed to investigate the use of texture as a tool for automatically determining homogeneous regions in a digitized historical book and segmenting its contents by extracting and analyzing texture features independently of the layout of the pages. The proposed framework is parameter-free and applicable to a large variety of ancient of books. It does not assume a priori information regarding document image content and structure. It consists of two phases: a texture-based feature extraction step and unsupervised clustering and labeling task based on the consensus clustering, hierarchical ascendant classification, and nearest neighbor search algorithms. The novelty of this work lies in the clustering of extracted texture descriptors to find automatically homogeneous regions, i.e. graphic and textual regions, by using the clustering approach on an entire book instead of processing each page individually. Our framework has been evaluated on a large variety of historical books and achieved promising results.

show abstract

Extracting Sentiment Networks from Shakespeare's Plays

Cited by 54 publications

References 13 publications

Multi‐level feature optimization and multimodal contextual fusion for sentiment analysis and emotion classification

Multi‐level feature optimization and multimodal contextual fusion for sentiment analysis and emotion classification

Proceedings of the Second Workshop on Storytelling

A texture-based pixel labeling approach for historical books

Contact Info

Product

Resources

About