The Detective Python

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Casing and barrel were embossed with His Majesty's crest. Among other celebrated recipients: The Python immediately made inroads into the law enforcement market when introduced, with the 6-inch barrel being popular with uniformed officers and the 4-inch barrel considered optimum for plainclothes use. A Python, loaded with. Colt's Python revolvers are still popular on the used market and command high prices.

Official Colt historian R. Hogg referred to it as the "best revolver in the world". The downside to the Colt Python's precision is its tendency to go "out of time" with continued heavy shooting. Author Martin Dougherty notes the weight of the Python as a drawback, as it is quite heavy for a handgun of its caliber, ranging from 2. This makes the Python comparable to Smith and Wesson's premier.

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Fall River Press, , page Abbeville Press, , P Standard Catalog of Colt Firearms. Update from Colt's Manufacturing Company, Inc. Some sentiment analysis tools can also factor in the emotional weight of other features of language such as punctuation or emojis. Sentiment analysis tools generally process a unit of text a sentence, paragraph, book, etc and output quantitative scores or classifications to indicate whether the algorithm considers that text to convey positive or negative emotion.

Some tools can also quantify the degree of positivity or degree of negativity within a text. Combined with other NLP methods like topic modeling , sentiment analysis provides a means of characterising the emotions expressed about different topics of conversation. When used in conjunction with network analysis it could shed light on the ways that individuals interact with one another.

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A researcher interested in attitudes towards a political event might use sentiment analysis to characterize how individuals describe that event on social media. Given the right data to input into the tool, it could be possible to make regional comparisons, or to understand how different demographics viewed the event differently. Because the tool can process lots of data sequentially, it is even possible to analyse the sentiment in hundreds of thousands or even millions of speech events.

To get you started, this lesson provides an introduction to sentiment analysis that is both practical and critical. Like any computational tool, sentiment analysis has a number of limitations and biases that researchers should take into account. Researchers should be especially cautious about making empirical claims based on the results of sentiment analysis.

You may be better served using sentiment analysis in provisional and exploratory situations, as a means for guiding the research process. When wielding these tools both skeptically and effectively, one can accomplish some pretty remarkable detective work.

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Written correspondences such as letters, e-mails, chat logs, tweets, and text message histories can provide researchers with invaluable insight into their authors. Texts are often rich with emotions and information not disclosed elsewhere. A researcher may learn about the opinions that their subjects held on various topics or about certain events.

It could also be possible to learn about the relationships that individuals developed and maintained within complex organizations or networks. While methodologies such as ethnography , close reading, and discourse analysis all help researchers analyze historical correspondence, these methods face significant challenges when the number of texts grows from dozens or hundreds to thousands or millions. Computational textual analysis provides a set of methods for making visible trends, dynamics, and relationships that may be hidden to the human reader by problems of scale.

Furthermore, many computation methods produce findings that can be expressed quantitatively, and that may subsequently allow the researcher to conduct statistical modeling , information visualization, and machine learning to make further discoveries. This tutorial uses the e-mail correspondence of bankrupt American energy company Enron. Enron concealed a wide variety of illegal accounting practices until a federal investigation in forced it into bankruptcy.

At the time, the Enron Scandal was the largest collapse of a publicly traded company in history. Securities and Exchange Commision SEC investigation revealed that Enron executives committed fraud and accounting malpractice on a massive scale.

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Enron declared bankruptcy in December of that year. In the years that followed, several executives faced criminial convictions for their role in the scandal. For researchers, the Enron Scandal resulted in the creation of one of the largest and most infamous correspondence text corpuses ever collected:.

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Over the following two years, the commission released, unreleased, and rereleased the e-mail corpus to the public after deleting e-mails that contained personal information like social security numbers. The Enron corpus contains e-mails whose subjects range from weekend vacation planning to political strategy talking points, and it remains the only large example of real world e-mail datasets available for research.

When the organized and redacted Enron E-mail Dataset was released in , researchers discovered an unprecedented opportunity: Suddenly, researchers had access to how people communicated at work at an unprecedented scale. This mattered for researchers interested in the special case of the Enron scandal and collapse, but also for researchers interested in a wide spectrum of questions about everyday communication at work.

In the following decade, hundreds of new studies sprouted up from the e-mails pursuing questions as diverse as social network theory , community and anomaly detection , gender and communication within organizations, behavioral change during an organizational crisis, and insularity and community formation. The use of social network theory in the humanities proposes some fascinating possibilities , but is not without significant debate.

In addition to the sheer quantity of messages included the corpus contains over , messages , the Enron E-mail Corpus also includes the metadata necessary for researchers to pursue a number of research questions. Just as the presence of envelopes with legible sender and recipient addresses would be a wonderful asset for researchers of historic letter correspondences, the presence of sender and recipient e-mail addresses allows researchers to associate e-mails with particular known individuals within the corporation. As some individuals had multiple e-mail addresses, or more than one individual may have shared the same address, the metadata is not fool proof, but it is incredibly insightful.

The rest of the tutorial will go through how to apply and interpret sentiment analysis of e-mails in this corpus. In this tutorial, you will be using Python along with a few tools from the Natural Language Toolkit NLTK to generate sentiment scores from e-mail transcripts. To do this, you will first learn how to load the textual data into Python, select the appropriate NLP tools for sentiment analysis, and write an algorithm that calculates sentiment scores for a given selection of text.

Finally, you will package your problem-solving algorithm as a self-contained bundle of code known as a function that you can reuse and repurpose including in part 2 of this tutorial. The Natural Language Toolkit NLTK is a collection of reusable Python tools also known as a Python library that help researchers apply a set of computational methods to texts.

The tools range from methods of breaking up text into smaller pieces, to identifying whether a word belongs in a given language, to sample texts that researchers can use for training and development purposes such as the complete text of Moby Dick. If you need any help downloading and installing the module for Python 3 , take a look at the Installing Python Modules with pip lesson by Fred Gibbs. NLTK is a very large toolkit, and several of its tools actually require a second download step to gather the necessary collection of data often coded lexicons to function correctly.

To install the sentiment analysis and word tokenizer we will use for this tutorial, write a new Python script with the following three lines:. Unlike other techniques that require training on related text before use, VADER is ready to go for analysis without any special setup. VADER is unique in that it makes fine-tuned distinctions between varying degrees of positivity and negativity.

It also attempts to capture and score textual features common in informal online text such as capitalizations, exclamation points, and emoticons, as shown in the table below:. Like any text analysis tool, VADER should be evaluated critically and in the context of the assumptions it makes about communication. VADER was developed in the mids primarily to analyse English language microblogging and social media sites especially Twitter.

This context is likely much more informal than professional e-mail, and contains language and feature usage patterns that differ from patterns when the Enron e-mails were written. Its sensitivity towards degrees of affect may be well-suited to describe the subtle displays of emotion within professional e-mail - as researchers, we may be especially interested in capturing the moments where emotion surfaces in otherwise formal text.

However, sentiment analysis continues to struggle to capture complex sentiments like irony, sarcasm, and mockery, when the average reader would be able to make the distinction between the literal text and its intended meaning. However, the developers encourage users to use automatic translation to pre-process non-English texts and then input the results into VADER. Implementation of this translation method is probably best reserved for intermediate Python users.

I am genuinely trying to be as reasonable as possible. Despite the feeling of frustration and anxiety you may glean from the paragraph as a whole, notice the ambivalence of the specific phrases within the paragraph. Some appear to express good faith efforts, e. This will be where we write the code for this task.