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Each of the tables provides the name of the measure, the purpose for which the measure was developed, and the targeted population. The tables also provide information on the method of assessment, the amount of time required to use the measure, and the page number where additional information is available. Part II also contains the review of each measure.

Measuring Computer Performance: A Practitioner's Guide by David J. Lilja

Part III provides examples of measures that can be copied for research or clinical purposes. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Buy eBook.

Measuring Computer Performance A Practitioner's Guide

These entities are known as named entities , which more specifically refer to terms that represent real-world objects like people, places, organizations, and so on, which are often denoted by proper names. A naive approach could be to find these by looking at the noun phrases in text documents. SpaCy has some excellent capabilities for named entity recognition. We can clearly see that the major named entities have been identified by spacy.

To understand more in detail about what each named entity means, you can refer to the documentation or check out the following table for convenience. For this, we will build out a data frame of all the named entities and their types using the following code. We can now transform and aggregate this data frame to find the top occuring entities and types. Do you notice anything interesting? Hint: Maybe the supposed summit between Trump and Kim Jong!

We can also group by the entity types to get a sense of what types of entites occur most in our news corpus. We can see that people, places and organizations are the most mentioned entities though interestingly we also have many other entities. Following code can be used as a standard workflow which helps us extract the named entities using this tagger and show the top named entities and their types extraction differs slightly from spacy.

We notice quite similar results though restricted to only three types of named entities. Interestingly, we see a number of mentioned of several people in various sports. Sentiment analysis is perhaps one of the most popular applications of NLP, with a vast number of tutorials, courses, and applications that focus on analyzing sentiments of diverse datasets ranging from corporate surveys to movie reviews. The key aspect of sentiment analysis is to analyze a body of text for understanding the opinion expressed by it.

Typically, we quantify this sentiment with a positive or negative value, called polarity. The overall sentiment is often inferred as positive , neutral or negative from the sign of the polarity score. Usually, sentiment analysis works best on text that has a subjective context than on text with only an objective context. Objective text usually depicts some normal statements or facts without expressing any emotion, feelings, or mood. Subjective text contains text that is usually expressed by a human having typical moods, emotions, and feelings.

Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment! Typically, sentiment analysis for text data can be computed on several levels, including on an individual sentence level, paragraph level, or the entire document as a whole. Often, sentiment is computed on the document as a whole or some aggregations are done after computing the sentiment for individual sentences.

There are two major approaches to sentiment analysis. For the first approach we typically need pre-labeled data. Hence, we will be focusing on the second approach. In this scenario, we do not have the convenience of a well-labeled training dataset. Hence, we will need to use unsupervised techniques for predicting the sentiment by using knowledgebases, ontologies, databases, and lexicons that have detailed information, specially curated and prepared just for sentiment analysis. A lexicon is a dictionary, vocabulary, or a book of words.

In our case, lexicons are special dictionaries or vocabularies that have been created for analyzing sentiments. Most of these lexicons have a list of positive and negative polar words with some score associated with them, and using various techniques like the position of words, surrounding words, context, parts of speech, phrases, and so on, scores are assigned to the text documents for which we want to compute the sentiment. After aggregating these scores, we get the final sentiment.

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Various popular lexicons are used for sentiment analysis, including the following. This is not an exhaustive list of lexicons that can be leveraged for sentiment analysis, and there are several other lexicons which can be easily obtained from the Internet. Feel free to check out each of these links and explore them. We will be covering two techniques in this section. The AFINN lexicon is perhaps one of the simplest and most popular lexicons that can be used extensively for sentiment analysis.

The author has also created a nice wrapper library on top of this in Python called afinn , which we will be using for our analysis. The following code computes sentiment for all our news articles and shows summary statistics of general sentiment per news category. We can get a good idea of general sentiment statistics across different news categories. Looks like the average sentiment is very positive in sports and reasonably negative in technology! We can see that the spread of sentiment polarity is much higher in sports and world as compared to technology where a lot of the articles seem to be having a negative polarity.

We can also visualize the frequency of sentiment labels.

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No surprises here that technology has the most number of negative articles and world the most number of positive articles. Sports might have more neutral articles due to the presence of articles which are more objective in nature talking about sporting events without the presence of any emotion or feelings.

Looks like the most negative article is all about a recent smartphone scam in India and the most positive article is about a contest to get married in a self-driving shuttle. Interestingly Trump features in both the most positive and the most negative world news articles. Do read the articles to get some more perspective into why the model selected one of them as the most negative and the other one as the most positive no surprises here! TextBlob is another excellent open-source library for performing NLP tasks with ease, including sentiment analysis.

It also an a sentiment lexicon in the form of an XML file which it leverages to give both polarity and subjectivity scores. Typically, the scores have a normalized scale as compare to Afinn. The polarity score is a float within the range [ The subjectivity is a float within the range [0. Looks like the average sentiment is the most positive in world and least positive in technology!

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However, these metrics might be indicating that the model is predicting more articles as positive. There definitely seems to be more positive articles across the news categories here as compared to our previous model. However, still looks like technology has the most negative articles and world, the most positive articles similar to our previous analysis. Well, looks like the most negative world news article here is even more depressing than what we saw the last time!

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The most positive article is still the same as what we had obtained in our last model. Finally, we can even evaluate and compare between these two models as to how many predictions are matching and how many are not by leveraging a confusion matrix which is often used in classification.

Looks like our previous assumption was correct. TextBlob definitely predicts several neutral and negative articles as positive.

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Overall most of the sentiment predictions seem to match, which is good! This was definitely one of my longer articles! If you are reading this, I really commend your efforts for staying with me till the end of this article. These examples should give you a good idea about how to start working with a corpus of text documents and popular strategies for text retrieval, pre-processing, parsing, understanding structure, entities and sentiment. We will be covering feature engineering and representation techniques with hands-on examples in the next article of this series.

Stay tuned! All the code and datasets used in this article can be accessed from my GitHub. The code is also available as a Jupyter notebook. I often mentor and help students at Springboard to learn essential skills around Data Science.

Measuring Computer Performance: A Practitioner’s Guide David J. Lilja

Thanks to them for helping me develop this content. The code is open-sourced on GitHub. Python 3.

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The code is open-sourced on GitHub for your convenience. If you have any feedback, comments or interesting insights to share about my article or data science in general, feel free to reach out to me on my LinkedIn social media channel. Thanks to Durba for editing this article. Sign in. Get started. Proven and tested hands-on strategies to tackle NLP tasks.