Jacky Casas

This project lies in the context of a collaboration between the HumanTech Institute and Kare Knowledgeware. Kare’s product is an automated knowledge retrieval conversational tool. The goal of the collaboration is to enhance the customer experience by adding empathy into the system. Empathy is possible by following two steps, first understanding the user intention, and second answering him accordingly.

The main objective is to develop a tool that will allow a user to extract useful information about his query: intent (informational vs emotional), sentiment (happy, upset). A user query enters the system, and this same query exits the system with annotations.

To achieve this goal, a system must be developed. This system has to:

1. Extract the intent from the query
2. Extract the emotion from the query

The current solution meets the initial objective. The intent classification model reaches 85% accuracy. The deep learning model for the emotion detection reaches 80% accuracy and the more simple model using hand-crafted features for the emotion detection reaches 60% accuracy, both on a problem consisting of 4 emotions so 4 classes: joy, anger, fear, sadness. Kare Knowledgeware can try these models on their data and see how the model performs. Hopefully, the models will perform well on their data too and they can work on answering user queries with more empathy.

Social networks, and digital communication in general, have evolved at an impressive speed in recent years. They have enabled everyone to stay in constant contact with family members, co-workers or classmates. This technological progress has also brought with it a number of disadvantages, one of them being cyberbullying.

Cyberbullying, which is simply bullying that occurs on digital devices, is primarily directed at teens. In the past, this problem was more or less limited to school boundaries. But unfortunately, technology has removed these boundaries and so the bullying continues unabated, leaving no respite for the victims. The consequences are numerous and this phenomenon has already led to many suicides. It is therefore necessary to be able to detect cyberbullying on social networks and take action accordingly.

During this project, it soon became clear that the lack of existing resources, including datasets containing relatively recent cyberbullying texts, would complicate the task. Therefore, a slightly different approach has been adopted. Indeed, the objective has been changed. It was no longer a question of detecting cyberbullying, but rather of finding out whether or not a text containing insults was hateful.

To do so, around 4’000 tweets have been collected and labelled. From this dataset, different features have been extracted and different predictions, mainly based on random forest and neural networks models, have been realized. This process made it possible to identify the most useful features which were none other than the TFIDF values. Combining these features with a few others made it possible to reach an accuracy of 72.76%, a relatively low score for a binary classification problem.

It is possible that the model currently in use relies too much on the statistics of the various insults. For example, if one insult appears predominantly in positive samples rather than negative ones, the model will have difficulty in correctly predicting the samples containing this insult but whose class should be positive. Of course, the opposite is also true.

The issue of the structuration of textual data is very paramount in today’s technological reality. Textual data is a part of our daily life, we emit it, text it, tweet it, and receive it in huge bulks regularly, making the realm of data largely dependent on textual content that is highly unstructured by nature and unexploitable. In a world where Machine Learning, Big Data, and smart assistants are becoming the trend, companies are relying on these concepts to flourish their businesses. Therefore, the need for a platform permitting textual analytics techniques for all, becomes essential. This is where Wisely comes in handy. In a nutshell, Wisely provides two of the most used branches of Natural Language Processing: Named Entity Recognition and Natural Language Understanding. Using our platform, a non-technical user can import their own dataset, do the wanted treatments and export the results for future usages. This report has the intention of helping you get a better understanding of how Wisely works by giving you its implementation details from all the aspects.

Multilingual Appointment Chatbot is a project in collaboration with a Swiss startup called Deeplink specialized in chatbot technologies. For one of their customers, Deeplink requires a chatbot being able to detect if there is a time and a date in a text message sent by a human to the bot and to respond with a proper answer. This chatbot will be used in order to take appointment with customers.

The goal of the project is to compare several popular Natural Language Processing al- gorithms with text in French translated by a translation service. After testing those algorithms, a scoreboard will be made with specific criteria to find the best viable solu- tion.

After that, it is planned to create a Telegram bot in order to interact with the com- pany schedule and the customer.

C’est là qu’intervient l’idée folle du projet Movie Dialog Bot. Afin de garder l’utilisateur captivé, le bot doit lui répondre par un message divertissant. L’idée est justement d’afficher à l’utilisa- teur une célèbre citation de film avec en plus, les informations concernant l’acteur, le caractère et le nom du film. La citation n’est pas affichée au hasard ! C’est à l’aide du machine learning qu’on va définir la meilleure citation à afficher en fonction de la phrase envoyée par l’utilisateur.

Il s’agit d’un chatbot qui vise à aider les personnes qui souffrent d’un trouble de comportement alimentaire (TCA). L’application se chargera d’interroger d’utilisateur afin de sauvegarder les détails de chaque crise dans une base de données, de proposer des stratégies de prévention adéquates ainsi que de rester à l’écoute de l’utilisateur en cas de problème.