Text classification a comprehensive how to save up money fast guide to classifying text with machine learning monkeylearn

Say that you want to classify news articles into 2 how to save up money fast groups, namely, sports and politics. First, you’ll need to define two lists of words that characterize how to save up money fast each group (e.G. Words related to sports such as football, basketball, lebron james, etc., and words related to politics such as donald trump, hillary clinton, putin, etc.). Next, when you want to classify a new incoming text, you’ll need to count the number of sport-related words that appear in the text and do the how to save up money fast same for politics-related words. If the number of sport-related word appearances is greater than the number of politics-related word count, then the text is classified as sports and vice versa.

Rule-based systems are human comprehensible and can be improved over how to save up money fast time. But this approach has some disadvantages. For starters, these systems require deep knowledge of the domain. They are also time-consuming, since generating rules for a complex system can be quite how to save up money fast challenging and usually requires a lot of analysis and testing. Rule-based systems are also difficult to maintain and don’t scale well given that adding new rules can affect how to save up money fast the results of the pre-existing rules. Machine learning based systems

All you need to know is that naive bayes is how to save up money fast based on bayes’s theorem, which helps us compute the conditional probabilities of occurrence of how to save up money fast two events based on the probabilities of occurrence of each how to save up money fast individual event. This means that any vector that represents a text will how to save up money fast have to contain information about the probabilities of appearance of how to save up money fast the words of the text within the texts of a how to save up money fast given category so that the algorithm can compute the likelihood how to save up money fast of that text’s belonging to the category. Support vector machines

Cross-validation is a common method to evaluate the performance of how to save up money fast a text classifier. It consists in splitting the training dataset randomly into equal-length sets of examples (e.G. 4 sets with 25% of the data). For each set, a text classifier is trained with the remaining samples (e.G. 75% of the samples). Next, the classifiers make predictions on their respective sets and the how to save up money fast results are compared against the human-annotated tags. This allows finding when a prediction was right (true positives and true negatives) and when it made a mistake (false positives, false negatives).

The answers to these questions can be found within the how to save up money fast sea of data available on social media, but without the help of computers, making sense of all this data manually would have to how to save up money fast be deemed impossible. Fortunately, machine learning makes it possible to analyze social media data how to save up money fast in a scalable and cost-effective way. You can leverage aspect-based sentiment analysis over a period of time to understand how to save up money fast what people are talking about on social media, how they are doing so and track trends over time. You can also use text classification for getting actionable insights how to save up money fast such as the following:

The online conversation around a brand and its competitors heavily how to save up money fast influences consumers. Some blogs, forums, review sites, and influencers are becoming more important than traditional outlets. According to minewhat, 81% of buyers conduct online research before making a purchase. Consumers care about what people are saying online about a how to save up money fast brand; brightlocal states that 85% of consumers trust online reviews as much as personal recommendations.

For instance, text classification is often used for automating ticket routing and how to save up money fast triaging. Imagine a global company that provides customer support in several how to save up money fast languages; this involves the process of assigning tickets based on the how to save up money fast ticket’s language. To do this, a person is needed to manually assign the ticket to how to save up money fast the correct team who can understand and reply to the how to save up money fast customer in the right language. With text classification, instead of using humans you can use a language detection how to save up money fast classifier to do this task for you.

Text classification can also be used for routing support tickets how to save up money fast to a teammate with specific product expertise. For instance, if a customer writes in asking about refunds, you can automatically assign the ticket to the teammate with how to save up money fast permission to perform refunds. This will ensure the customer gets a quality response more how to save up money fast quickly. Without the need for triaging every single ticket, support teams can work more efficiently and reduce response times. You’ll always know that tickets have been routed to the how to save up money fast team that needs to answer them.

To find out, we experimented with keyword extraction and sentiment analysis to analyze how to save up money fast 200,000+ customer interactions with verizon, T-mobile, AT&T, and sprint on twitter. First, we analyzed the most relevant keywords in all these tweets how to save up money fast and found out that each carrier has its unique approach how to save up money fast towards interacting with customers. For instance, T-mobile has a friendlier and more personal approach, with every support representative signing each message with their name, while verizon tweets are very dry and professional. Then, we performed sentiment analysis on the data, and the results suggest that a friendlier take on social how to save up money fast media elicits more positive responses:

To be able to do this, the information gathered, which usually involves open-ended responses, must be processed. By manually annotating responses into different categories, product teams can identify valuable insights and trends over time. The problem is that this manual process is tedious and how to save up money fast very time-consuming. That’s when text classification comes in. Instead of relying on humans to do this task, you can quickly process customer feedback with machine learning. Classification models can help you analyze survey results to discover how to save up money fast patterns and insights like:

You can use internal data generated from the apps and how to save up money fast tools that you use every day such as crms (e.G. Salesforce, hubspot), chat apps (e.G. Slack, drift, intercom), help desk software (e.G. Zendesk, freshdesk, front), survey tools (e.G. SurveyMonkey, typeform, google forms), and customer satisfaction tools (e.G. Promoter.Io, retently, satismeter). These tools usually provide an option to export data in how to save up money fast a CSV file that you could use for training your how to save up money fast classifier.

One of the reasons machine learning is becoming mainstream is how to save up money fast because of the myriad of open source libraries available for how to save up money fast developers interested in applying it. Although they still require machine learning knowledge for building and how to save up money fast deploying models, these libraries offer a fair level of abstraction and simplification. Python, java, and R all offer a wide selection of machine learning how to save up money fast libraries that are actively developed and provide a diverse set how to save up money fast of features, performance, and capabilities. Text classification with python

Once you are ready to experiment with more complex algorithms, you should check out deep learning libraries like keras, tensorflow, and pytorch. Keras is probably the best starting point as its designed how to save up money fast to simplify the creation of recurrent neural networks (rnns) and convolutional neural networks (cnns). TensorFlow is the most popular open source library for implementing how to save up money fast deep learning algorithms. Developed by google and used by companies such as dropbox, ebay, and intel, this library is optimized for setting up, training, and deploying artificial neural networks with massive datasets. Although it’s harder to master than keras, it’s the undisputed leader in the deep learning space. A reliable alternative to tensorflow is pytorch, an extensive deep learning library primarily developed by facebook and how to save up money fast backed by twitter, nvidia, salesforce, stanford university, university of oxford, and uber. Text classification with java

Well, if you want to avoid these hassles, a great alternative is to use a software as a how to save up money fast service (saas) for text classification which usually solves most of the problems how to save up money fast mentioned above. Another advantage is that they don’t require machine learning experience and even people who don’t know how to code can use and consume text how to save up money fast classifiers. At the end of the day, leaving the heavy lifting to a saas can save you how to save up money fast time, money, and resources when implementing a text classification system.

MonkeyLearn provides some useful tools for understanding how well the how to save up money fast model is working such as classifier stats (e.G. Accuracy, F1 score, precision, and recall) and a keyword cloud of n-grams for each category. There are multiple ways for improving the accuracy of your how to save up money fast classifier, including tagging more training data, going through the false positives and false negatives and retag how to save up money fast the incorrectly labeled examples, and cleaning your data to disassociate keywords with a specific how to save up money fast tag. Integrating the classifier

RELATED_POSTS