Fine-Grained Emotion Detection in Contact Center Chat Utterances

Mundra, Shreshtha; Sen, Anirban; Sinha, Manjira; Mannarswamy, Sandya; Dandapat, Sandipan; Roy, Shourya

doi:10.1007/978-3-319-57529-2_27

Cited by 20 publications

(16 citation statements)

References 12 publications

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“…It is worth noting that textual dialogues are informal and laden with misspellings which pose serious challenges for automatic emotion detection approaches. Prior to this task, to the best of our knowledge, the methods proposed by Mundra et al (2017) and Chatterjee et al (2019) are some of the few methods that tackled the problem of emotion detection in English textual dialogues.…”

Section: Related Workmentioning

confidence: 99%

SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

Chatterjee¹,

Narahari²,

Joshi³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

214

124

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In this paper, we present the SemEval-2019 Task 3-EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading "Why don't you ever text me!" we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes-Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.

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Section: Related Workmentioning

confidence: 99%

SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

Chatterjee¹,

Narahari²,

Joshi³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

214

124

View full text Add to dashboard Cite

show abstract

“…Train Dev Test Neural, deep-learning based approaches use architectures such as variations of recurrent models: GRU (Chung et al, 2014), LSTM (Hochreiter and Schmidhuber, 1997), BiLSTM (Schuster and Paliwal, 1997) and Convolutional models (Mundra et al, 2017), performing significantly better than other machine-learning techniques. Their ability to generalise and capture context over long se- quences makes them a popular choice for text classification tasks.…”

Section: Statisticmentioning

confidence: 99%

NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep

Agrawal

Suri

2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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Existing Machine Learning techniques yield close to human performance on text-based classification tasks. However, the presence of multi-modal noise in chat data such as emoticons, slang, spelling mistakes, code-mixed data, etc. makes existing deep-learning solutions perform poorly. The inability of deeplearning systems to robustly capture these covariates puts a cap on their performance. We propose NELEC : Neural and Lexical Combiner, a system which elegantly combines textual and deep-learning based methods for sentiment classification. We evaluate our system as part of the third task of 'Contextual Emotion Detection in Text' as part of SemEval-2019(Chatterjee et al., 2019b. Our system performs significantly better than the baseline, as well as our deeplearning model benchmarks. It achieved a micro-averaged F 1 score of 0.7765, ranking 3 rd on the test-set leader-board. Our code is available at https://github.com/ iamgroot42/nelec

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“…For this reason, artificial intelligence is regarded as an essential component for process maintenance and optimization in contact centers, with great emphasis on natural language modeling [18]. As a result, recent research has widely focused on oral and written conversations between customers and agents [4,7,23,28,37,38].…”

Section: Introductionmentioning

confidence: 99%

Modeling Customer Experience in a Contact Center through Process Log Mining

Fu¹,

Zampieri

Hodgson³

et al. 2021

ACM Trans. Intell. Syst. Technol.

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The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already established pipelines often must adapt to the way data is sampled and to its content. In this study, we tackle the challenge of characterizing and predicting customer experience having available only process log data with time-stamp information, without any ground truth feedback from the customers. As a case study, we consider the context of a contact center managed by TeleWare and analyze phone call logs relative to a two months span. We develop an approach to interpret the phone call process events registered in the logs and infer concrete points of improvement in the service management. Our approach is based on latent tree modeling and multi-class Naïve Bayes classification, which jointly allow us to infer a spectrum of customer experiences and test their predictability based on the current data sampling strategy. Moreover, such approach can overcome limitations in customer feedback collection and sharing across organizations, thus having wide applicability and being complementary to tools relying on more heavily constrained data.

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Fine-Grained Emotion Detection in Contact Center Chat Utterances

Cited by 20 publications

References 12 publications

SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep

Modeling Customer Experience in a Contact Center through Process Log Mining

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