A Mood-based Genre Classification of Television Content

Humberto Corona; Michael P. O'Mahony

RECORD

A Mood-based Genre Classification of Television Content

Humberto Corona, Michael P. O'Mahony

Information Retrieval · Submitted 6 Aug 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our experiments show that, over a large collection of television content, program genres can be represented in a three-dimensional space of valence, arousal and dominance, and that promising classification results can be achieved using features based on this representation.

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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,883 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.08 · human participants 0.06 · controlled comparison 0.14 · randomized assignment 0.01 · states quantitative result 0.30 · compares with prior results 0.37 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; show every role.

The classification of television content helps users organise and navigate through the large list of channels and programs now available. In this paper, we address the problem of television content classification by exploiting text information extracted from program transcriptions. We present an analysis which adapts a model for sentiment that has been widely and successfully applied in other fields such as music or blog posts. We use a real-world dataset obtained from the Boxfish API to compare the performance of classifiers trained on a number of different feature sets. Our experiments show that, over a large collection of television content, program genres can be represented in a three-dimensional space of valence, arousal and dominance, and that promising classification results can be achieved using features based on this representation. This finding supports the use of the proposed representation of television content as a feature space for similarity computation and recommendation generation.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	The classification of television content helps users organise and navigate through the large list of channels and programs now available.	background 1.00	0.04	0.02	asserted 1.00
s1	In this paper, we address the problem of television content classification by exploiting text information extracted from program transcriptions.	contribution 0.86	0.74	0.03	asserted 0.88
s2	We present an analysis which adapts a model for sentiment that has been widely and successfully applied in other fields such as music or blog posts.	method 0.76	0.70	0.03	asserted 0.97
s3	We use a real-world dataset obtained from the Boxfish API to compare the performance of classifiers trained on a number of different feature sets.	method 1.00	0.91	0.02	asserted 0.93
s4	Our experiments show that, over a large collection of television content, program genres can be represented in a three-dimensional space of valence, arousal and dominance, and that promising classification results can be achieved using features based on this representation.	result 1.00	0.97	0.84	asserted 0.97
s5	This finding supports the use of the proposed representation of television content as a feature space for similarity computation and recommendation generation.	interpretation 0.99	0.62	0.40	asserted 0.54

What kind of paper the classifier reports this is: empirical study 0.54 · evidence from observation or experiment 0.58.

What was examined, and what was not

sources read

arXiv listing abstract v1

not examined

full text
figures and tables
references and citing sentences
relations to other papers

levels run

P abstract

not run

S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

The classification of television content helps users organise and navigate through the large list of channels and programs now available. In this paper, we address the problem of television content classification by exploiting text information extracted from program transcriptions. We present an analysis which adapts a model for sentiment that has been widely and successfully applied in other fields such as music or blog posts. We use a real-world dataset obtained from the Boxfish API to compare the performance of classifiers trained on a number of different feature sets. Our experiments show that, over a large collection of television content, program genres can be represented in a three-dimensional space of valence, arousal and dominance, and that promising classification results can be achieved using features based on this representation. This finding supports the use of the proposed representation of television content as a feature space for similarity computation and recommendation generation.

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