J48S: A Sequence Classification Approach to Text Analysis Based on Decision Trees

Brunello, Andrea; Marzano, Enrico; Montanari, Angelo; Sciavicco, Guido

doi:10.1007/978-3-319-99972-2_19

Cited by 7 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unstructured text or conversation data present the additional challenge of being converted to processable information. To that end, embedding semantic knowledge in sequential word data based on information gain has been shown to provide competitive and interpretable models while alleviating the burden of data preparation [7]. On the other hand, raw conversation data were successfully used to predict customer satisfaction by means of artificial neural networks [37].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Customer Experience in a Contact Center through Process Log Mining

Fu¹,

Zampieri

Hodgson³

et al. 2021

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

“…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.

View full text Add to dashboard Cite

show abstract

“…Finally, on the basis of the results obtained from the application of the linguistic and classical machine learning approaches, we assess the impact on the text analysis process of a solution based on a decision tree induction algorithm capable of seamlessly handling sequential data (Brunello et al, 2018). The outcome of the evaluation is that such a solution has at least two major key benefits: the simplification of the data preparation phase and the high interpretability of the final model which, as we have already pointed out, is one of our main goals.…”

Section: Introductionmentioning

confidence: 99%

A combined approach to the analysis of speech conversations in a contact center domain

Brunello¹,

Marzano²,

Montanari³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The ever more accurate search for deep analysis in customer data is a really strong technological trend nowadays, quite appealing to both private and public companies. This is particularly true in the contact center domain, where speech analytics is an extremely powerful methodology for gaining insights from unstructured data, coming from customer and human agent conversations. In this work, we describe an experimentation with a speech analytics process for an Italian contact center, that deals with call recordings extracted from inbound or outbound flows. First, we illustrate in detail the development of an in-house speech-to-text solution, based on Kaldi framework, and evaluate its performance (and compare it to Google Cloud Speech API). Then, we evaluate and compare different approaches to the semantic tagging of call transcripts, ranging from classic regular expressions to machine learning models based on ngrams and logistic regression, and propose a combination of them, which is shown to provide a consistent benefit. Finally, a decision tree inducer, called J48S, is applied to the problem of tagging. Such an algorithm is natively capable of exploiting sequential data, such as texts, for classification purposes. The solution is compared with the other approaches and is shown to provide competitive classification performances, while generating highly interpretable models and reducing the complexity of the data preparation phase. The potential operational impact of the whole process is thoroughly examined.

show abstract

“…This paper presents J48SS, a novel decision tree learner based on WEKA's J48 (a Java implementation of C4.5 [1]) (preliminary conference versions of some of its contributions can be found in [2,3]). The algorithm is capable of naturally exploiting categorical, numerical, sequential, and time series data during the same execution cycle.…”

Section: Introductionmentioning

confidence: 99%

J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data

Brunello

Marzano²,

Montanari

et al. 2019

Computers

Self Cite

View full text Add to dashboard Cite

Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. This paper presents J48SS, a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The novel algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm is evaluated on a text classification task in a real business setting, as well as on a selection of public UCR time series datasets. Results show that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort.

show abstract

J48S: A Sequence Classification Approach to Text Analysis Based on Decision Trees

Cited by 7 publications

References 22 publications

Modeling Customer Experience in a Contact Center through Process Log Mining

Modeling Customer Experience in a Contact Center through Process Log Mining

A combined approach to the analysis of speech conversations in a contact center domain

J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data

Contact Info

Product

Resources

About