Choosing the best language to build your AI chatbot

On the subject of machine learning, what better approach than to look at some hard data to see which language the experts prefer? In a recent survey of more than 2,000 data scientists and machine learning developers, more than 57 percent of them used Python, while 33 percent prioritized it for development. With regards to natural language processing (NLP), the grandfather of NLP integration was written in Python. Natural Language Toolkit’s (NLTK) initial release was in 2001 — five years ahead of its Java-based competitor Stanford Library NLP — serving as a wide-ranging resource to help your chatbot utilize the best functions of NLP.

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Its main weaknesses are its limited community for support and the fact that it is only available in English. However, if your chatbot is for a smaller company that does not require multiple languages, it offers a compelling choice. The classifier is based on the Naive Bayes Classifier, which can look at the feature set of a comment to calculate how likely a certain sentiment is by analyzing prior probability and the frequency of words. Once completed, we use a feature extractor to create a dictionary of the remaining relevant words to create our finished training set, which is passed to the classifier. Sentiment analysis in its most basic form involves working out whether the user is having a good experience or not. If a chatbot is able to recognize this, it will know when to offer to pass the conversation over to a human agent, which products users are more excited about or which opening line works best.

Choosing the best language to build your AI chatbot

Where Weka struggles compared to its Python-based rivals is in its lack of support and its status as more of a plug and play machine learning solution. This is great for small data sets and more simple analyses, but Python’s libraries are much more practical. If speed is your main concern with chatbot building you will also be found wanting with Python in comparison to Java and C++. Of more importance is the end-user experience, and picking a faster but more limited language for chatbot-building such as C++ is self-defeating. For this reason, sacrificing development time and scope for a bot that might function a few milliseconds more quickly does not make sense.

It also is one of the easier languages for a beginner to pick up with its consistent syntax and language that mirrors humans. Of course, the caveat should always be to veer toward the language you are most comfortable with, but for those dipping their toe into the programming pond for the first time, a clear winner starts to emerge. No, this is not about whether you want your virtual agent to understand English slang, the subjunctive tense in Spanish or even the dozens of ways to say “I” in Japanese. In fact, the programming language you build your bot with is as important as the human language it understands.

Similar to NLP, Python boasts a wide array of open-source libraries for chatbots, including scikit-learn and TensorFlow. Scikit-learn is one of the most advanced out there, with every machine learning algorithm for Python, while TensorFlow is more low-level — the LEGO blocks of machine learning algorithms, if you like. NLTK is not only a good bet for fairly simple chatbots, but also if you are looking for something more advanced. From here a whole world of other Python libraries is opened up to you, including many that specialize in machine learning. An interesting rival to NLTK and TextBlob has emerged in Python (and Cython) in the form of spaCy.

Stanford NLP and Apache Open NLP offer an interesting alternative for Java users, as both can adequately support chatbot development either through tooling or can be explicitly used when calls are made via APIs. But NLTK is superior thanks to its additional support for other languages, multiple versions and interfaces for other NLP tools and even the capability to install some Stanford NLP packages and third-party Java projects. Python’s biggest failing lies in its documentation, which pales in comparison to other established languages such as PHP, Java and C++. Searching for answers within Python is akin to finding a specific passage in a book you have never read. In addition, the language is severely lacking in useful and simple examples. Clarity is also an issue, which is incredibly important when building a chatbot, as even the slightest ambiguity within one of the steps could cause it to fail.

• Python’s biggest failing lies in its documentation, which pales in comparison to other established languages such as PHP, Java and C++.
• This is great for small data sets and more simple analyses, but Python’s libraries are much more practical.
• Once completed, we use a feature extractor to create a dictionary of the remaining relevant words to create our finished training set, which is passed to the classifier.
• JavaScript contains a number of libraries, as outlined here for demonstration purposes, while Java lovers can rely on ML packages such as Weka.

This meant that when Python was first released it was applied to more diverse cases than other languages such as Ruby, which was restricted to web design and development. Meanwhile, Python expanded in scientific computing, which encouraged the creation of a wide range of open-source libraries that have benefited from years of R&D. But if you are starting out fresh and are wondering which language is worth investigating first to give your chatbot a voice, following the data science crowd and looking at Python is a good start. Facebook, Slack and Telegram all support the most popular languages, while API platforms such as Dialogflow, LUIS and wit.ai offer SDKs for the majority. Let’s take a look at one aspect of NLP to see how useful Python can be when it comes to making your chatbot smart. Python is essentially the Swiss Army Knife of coding thanks to its versatility.

PHP, for one, has little to offer in terms of machine learning and, in any case, is a server-side scripting language more suited to website development. C++ is one of the fastest languages out there and is supported by such libraries as TensorFlow and Torch, but still lacks the resources of Python. Java and JavaScript both have certain capabilities when it comes to machine learning. JavaScript contains a number of libraries, as outlined here for demonstration purposes, while Java lovers can rely on ML packages such as Weka.

Namely, that it implements a single stemmer rather than the nine stemming libraries on offer with NLTK. This is a problem when deciding which one is most effective for your chatbot. As seen here, spaCy is also lightning fast at tokenizing and parsing compared to other systems in other languages.