Data-flow analysis is a technique for gathering information about the possible set of values calculated at various points in a computer program.A program's control-flow graph (CFG) is used to determine those parts of a program to which a particular value assigned to a variable might propagate. The information gathered is often used by compilers when optimizing a program INTRODUCTION. Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test Also, secondary data needs to be integrated in data analysis in a logical and unbiased manner. Let’s take another example. You are writing a dissertation exploring the impacts of foreign direct investment (FDI) on the levels of economic growth in Vietnam using correlation quantitative data analysis method. You have specified FDI and GDP as
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Sign in. Oct 1, · 8 min read. Thus deriving information from unstruc t ured data is an essential part of data analysis. Text mining is the process of deriving valuable insights from unstructured text data, data analysis of dissertation, and sentiment analysis is one applicant of text mining.
It is using natural language processing and machine learning techniques to understand and classify subjective emotions from text data. In business settings, sentiment analysis is widely used in understanding customer reviews, detecting spam from emails, etc. This article is the first part of the tutorial that introduces the specific techniques used to conduct sentiment analysis with Python.
To illustrate the procedures better, I will use one of my projects as an example, where I conduct news sentiment analysis on WTI crude oil future prices.
I will present the important steps along with the corresponded Python code. Some background information. The crude oil future prices have large short-run fluctuations. While the long-run equilibrium of any product is determined by the demand and supply conditions, the short-run fluctuations in prices are reflections of the market confidence and expectations toward this product. In this project, I use crude oil-related news articles to capture constantly updating market confidence and expectations, and predict the change of crude oil future prices by conducting sentiment analysis on news articles.
Here are the steps to complete this analysis:. I will discuss the second part, which is preprocessing the text data in this article. If you are interested in other parts, please follow the links to read more coming up.
Preprocessing text data, data analysis of dissertation. I use tools from NLTK, Spacy, and some regular expressions to preprocess the news articles. To import the libraries and use the pre-built models in Spacy, you can use the following code:. Afterwards, I use pandas to read in the data:. I preprocessed the news articles following the standard text mining procedures to extract useful features from the news contents, including tokenization, removing stopwords, and lemmatization.
The first step of preprocessing text data is to break every sentence into individual words, which is called tokenization. Taking individual words rather than sentences breaks down the connections between words.
However, it is a common method used to analyze large sets of text data. It data analysis of dissertation efficient and convenient for computers to analyze the text data by examines what words appear in an article and how many times these words appear, and is sufficient to give insightful results.
Take the first news article in my dataset as an example:. You can use the NLTK tokenizer:. Or you can use Spacy, remember NLP is the Spacy engine defined above:. After tokenization, each news article will transform into a list of words, symbols, digits, data analysis of dissertation, and punctuation.
You can specify whether you want to transform every word into a lowercase as well. The next step is to remove useless information. For example, symbols, data analysis of dissertation, digits, punctuations. I will use spacy combined with regex to remove them. After applying the transformations above, this is how the original news article looks like:. The next step is to remove the useless words, namely, the stopwords. Stopwords are words that frequently appear in many articles, but without significant meanings.
These are the words that will not intervene in the understanding of articles if removed. Data analysis of dissertation remove the stopwords, we can data analysis of dissertation the stopwords from the NLTK library. Besides, I also include other lists of stopwords that are widely used in economic analysis, including dates and time, more general words that are not economically meaningful, etc.
This is how I construct the list of stopwords:. and then exclude the stopwords from the news articles:. Applying to the previous example, this is how it looks like:. Removing stopwords, along with symbols, digits, data analysis of dissertation, and punctuation, each news article will transform into a list of meaningful words. However, to count the appearance of each word, it is essential to remove grammar tense and transform each word into its original form.
Thus, lemmatization is an essential step for text transformation. Another way of converting words to its original form is called stemming. Here is the difference between them:. Lemmatization is taking a word into its original lemma, and stemming is taking the linguistic root of a word. I choose lemmatization over stemming because after stemming, some words become hard to understand.
For the interpretation purpose, the lemma is better than the linguistic root. As shown above, lemmatization is very easy to implement with Spacy, where I call the. After lemmatization, each news article will transform into a list of words that are all in their original forms.
The news article now changed into this:. Summarize the steps. Before generalizing into all news articles, it is important to apply it on random news articles and see how it works, data analysis of dissertation, following the code below:.
If there are some extra words you want to exclude for this particular project or some extra redundant information you want to remove, you can always revise the function before applying to all news articles. Here is a piece of randomly selected news article before and after tokenization, removing stopwords and lemmatization.
If all looks great, you can apply the function to all news articles:. Some remarks. Text preprocessing is a very important part of text mining and sentiment analysis. There are a lot of ways of preprocessing the unstructured data to make it readable for data analysis of dissertation for future analysis. For the next data analysis of dissertation, I will discuss the vectorizer I used to transform text data into a sparse matrix so that they can be used as input for quantitative analysis.
If your analysis is simple and does not require a lot of customization in preprocessing the text data, the vectorizers usually have embedded functions to conduct the basic steps, like tokenization, data analysis of dissertation, removing stopwords.
Or you can write your own function and specify your customized function in the vectorizer so you can preprocess and vectorize your data at the same time, data analysis of dissertation.
If you want this way, your function needs to return a list of tokenized words rather than a long string. However, personally speaking, I prefer to preprocess the text data first before vectorization. In this way, I keep monitoring the performance of my function, and it is actually faster especially if you have a data analysis of dissertation data set. I will discuss the transformation process in my next article.
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Data-flow analysis is a technique for gathering information about the possible set of values calculated at various points in a computer program.A program's control-flow graph (CFG) is used to determine those parts of a program to which a particular value assigned to a variable might propagate. The information gathered is often used by compilers when optimizing a program Jul 08, · Applying Data Analysis to Your Dissertation. When done appropriately, data analysis can provide a solid foundation for the results and discussion sections of your doctoral dissertation. Therefore, it is imperative to conduct thorough and careful data analysis in order to derive meaningful and insightful findings Oct 01, · It is estimated that 80% of the world’s data is unstructured. Thus deriving information from unstruc t ured data is an essential part of data analysis. Text mining is the process of deriving valuable insights from unstructured text data, and sentiment analysis is one applicant of text mining
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