This article shows how you can perform sentiment analysis on Twitter tweets using Python and Natural Language Toolkit (NLTK). Sentiment Analysis with Python NLTK Text Classification. For each review, I removed punctuations, tokenized the string, removed stop words. Sentiment Analysis 1 - Data Loading with Pandas. Techopedia defines sentiment analysis as follows: Sentiment analysis is a type of data mining that measures the inclination of people’s opinions through natural language processing (NLP), computational linguistics and text analysis, which are used to extract and analyze subjective information from the Web — mostly social media and similar sources. Here in America , we have labored long and hard to, # Equivalent to fd = nltk.FreqDist(words), [(('the', 'United', 'States'), 294), (('the', 'American', 'people'), 185)], ('the', 'United', 'States') ('the', 'American', 'people'), {'neg': 0.0, 'neu': 0.295, 'pos': 0.705, 'compound': 0.8012}, """True if tweet has positive compound sentiment, False otherwise. We start our analysis by creating the pandas data frame with two columns, tweets and my_labels which take values 0 (negative) and 1 (positive). Feature engineering is a big part of improving the accuracy of a given algorithm, but it’s not the whole story. What’s your #1 takeaway or favorite thing you learned? Tags: #English #NLP. Instead, make a list of the file IDs that the corpus uses, which you can use later to reference individual reviews: .fileids() exists in most, if not all, corpora. To do this, we're going to combine this tutorial with the Twitter streaming API tutorial . Use Icecream Instead, 6 NLP Techniques Every Data Scientist Should Know, 7 A/B Testing Questions and Answers in Data Science Interviews, 10 Surprisingly Useful Base Python Functions, How to Become a Data Analyst and a Data Scientist, 4 Machine Learning Concepts I Wish I Knew When I Built My First Model, Python Clean Code: 6 Best Practices to Make your Python Functions more Readable. With these tools, you can start using NLTK in your own projects. As the name implies, this is a collection of movie reviews. It goes like this: “Everything was beautiful and nothing hurt” — Kurt Vonnegut. For example, "This is awesome!" In a rule-based NLP study for sentiment analysis, we need a lexicon that serves as a reference manual to measure the sentiment of a chunk of text (e.g., word, phrase, sentence, paragraph, full text). If I hadn’t mentioned the nature of his work earlier I am guessing most humans would consider this quote to have positive sentiment. You can analyze bodies of text, such as comments, tweets, and product reviews, to obtain insights from your audience. NLTK provides a number of functions that you can call with few or no arguments that will help you meaningfully analyze text before you even touch its machine learning capabilities. intermediate Should NLTK require additional resources that you haven’t installed, you’ll see a helpful LookupError with details and instructions to download the resource: The LookupError specifies which resource is necessary for the requested operation along with instructions to download it using its identifier. I am now interested to explore detecting sarcasm or satire in a text. You’ll begin by installing some prerequisites, including NLTK itself as well as specific resources you’ll need throughout this tutorial. The possibilities are endless! [nltk_data] Unzipping corpora/twitter_samples.zip. Sentiment analysis is the practice of using algorithms to classify various samples of related text into overall positive and negative categories. Have a look at your list. [nltk_data] Downloading package vader_lexicon to. Sentiment analysis is a subfield or part of Natural Language Processing (NLP) that can help you sort huge volumes of unstructured data, from online reviews of your products and services (like Amazon, Capterra, Yelp, and Tripadvisor to NPS responses and conversations on social media or all over the web.. Twitter Sentiment Analysis. We start our analysis by creating the pandas data frame with two columns, tweets and my_labels which take values 0 (negative) and 1 (positive). While this doesn’t mean that the MLPClassifier will continue to be the best one as you engineer new features, having additional classification algorithms at your disposal is clearly advantageous. You can focus these subsets on properties that are useful for your own analysis. While this will install the NLTK module, you’ll still need to obtain a few additional resources. For the small scope of the project and also as guided by the tutorial, I selected only adjectives from the features based on the assumption that adjectives are highly informative of positive and negative sentiments. In this example, we develop a binary classifier using the manually generated Twitter data to detect the sentiment of each tweet. By using the predefined categories in the movie_reviews corpus, you can create sets of positive and negative words, then determine which ones occur most frequently across each set. NLTK includes pre-trained models in addition to its text corpus. If all you need is a word list, there are simpler ways to achieve that goal. All you have to do is initiate the NLTK SkleanClassifier with the specific Scikit Learn module as a parameter. Remember that punctuation will be counted as individual words, so use str.isalpha() to filter them out later. By the end of this tutorial, you’ll be ready to: Free Bonus: Click here to get our free Python Cheat Sheet that shows you the basics of Python 3, like working with data types, dictionaries, lists, and Python functions. Next, to pick the most informative adjectives I created a frequency distribution of the words in all_words, using nltk.FreqDist() method. The figure on the right shows both the confusion matrix for the prediction without and with normalization. How are you going to put your newfound skills to use? Sentiment analysis can help you determine the ratio of positive to negative engagements about a specific topic. This post describes the implementation of sentiment analysis of tweets using Python and the natural language toolkit NLTK. Process: The Algorithm : Tokenize, clean and lemmatize the data and took only the adjectives from the reviews. What is sentiment analysis - A practitioner's perspective: Essentially, sentiment analysis or sentiment classification fall into the broad category of text classification tasks where you are supplied with a phrase, or a list of phrases and your classifier is supposed to tell if the sentiment behind that is positive, negative or neutral. Sentiment Analysis >>> from nltk.classify import NaiveBayesClassifier >>> from nltk.corpus import subjectivity >>> from nltk.sentiment import SentimentAnalyzer >>> from nltk.sentiment.util import * For example, the name of an actress in a review would not give any information about the sentiment of a review. With NLTK, you can employ these algorithms through powerful built-in machine learning operations to obtain insights from linguistic data. A 64 percent accuracy rating isn’t great, but it’s a start. NLTK consists of the most common algorithms such as tokenizing, part-of-speech tagging, stemming, sentiment analysis, topic segmentation, and named entity recognition. 2. 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. Unsubscribe any time. This degree is measured as (Number of winning votes)/Total Votes. I called this list ‘all_words’ and it needs another round of filtering still. You’ll need to obtain that specific review using its file ID and then split it into sentences before rating: .raw() is another method that exists in most corpora. Although computers cannot identify and process the string inputs, the libraries like NLTK, TextBlob and many others found a way to process string mathematically. “ When captured electronically, customer sentiment — expressions beyond facts, that convey mood, opinion, and emotion — carries immense business value. is positive, negative, or neutral. MNB: 0.845, BNB: 0.8447999, LogReg: 0.835, SGD: 0.8024, SVC: 0.7808. Natural Language ToolKit (NLTK) is one of the popular packages in Python that can aid in sentiment analysis. In fact, it’s important to shuffle the list to avoid accidentally grouping similarly classified reviews in the first quarter of the list. In the world of machine learning, these data properties are known as features, which you must reveal and select as you work with your data. As you probably noticed, this new data set takes even longer to train against, since it's a larger set. The following classifiers are a subset of all classifiers available to you. These ratios are known as likelihood ratios. One of them is .vocab(), which is worth mentioning because it creates a frequency distribution for a given text. Since you’re looking for positive movie reviews, focus on the features that indicate positivity, including VADER scores: extract_features() should return a dictionary, and it will create three features for each piece of text: In order to train and evaluate a classifier, you’ll need to build a list of features for each text you’ll analyze: Each item in this list of features needs to be a tuple whose first item is the dictionary returned by extract_features and whose second item is the predefined category for the text. Curated by the Real Python team. No spam ever. Sentiment Analysis 1 - Data Loading with Pandas. While tokenization is itself a bigger topic (and likely one of the steps you’ll take when creating a custom corpus), this tokenizer delivers simple word lists really well. Twitter Sentiment Analysis with NLTK Now that we have a sentiment analysis module, we can apply it to just about any text, but preferrably short bits of text, like from Twitter! The list to above (left) shows 15 of the most informative features from the model. Sarcasm is subtle and even humans find it hard to pick up. Sentiment analysis, opinion mining call it what you like, if you have a product/service to sell you need to be on it. This is one example of a feature you can extract from your data, and it’s far from perfect. Building a corpus can be as simple as loading some plain text or as complex as labeling and categorizing each sentence. For this, sentiment analysis can help. However, as the size of your audience increases, it becomes increasingly difficult to understand what your users are saying. Instead, this is really just plain impossible, seeing as how it’s rarely the case that 80% of people agree on the sentiment of text. 2. The purpose of the implementation is to be able to automatically classify a tweet as a positive or negative tweet sentiment wise. In addition to these two methods, you can use frequency distributions to query particular words. A frequency distribution is essentially a table that tells you how many times each word appears within a given text. The negative, neutral, and positive scores are related: They all add up to 1 and can’t be negative. In this section, you’ll learn how to integrate them within NLTK to classify linguistic data. 3. Next, I wanted to see if the predictive power of all these models were combined, so to speak, could we reach a better score? The special thing about this corpus is that it’s already been classified. Sentiment Analysis is the analysis of the feelings (i.e. 2y ago. A supervised learning model is only as good as its training data. I intentionally took two reviews that were not as polarizing and two that were very polarizing to see how the model performs. You’ll also be able to leverage the same features list you built earlier by means of extract_features(). Now you’ve reached over 73 percent accuracy before even adding a second feature! To do this, we're going to combine this tutorial with the Twitter streaming API tutorial . A Sentiment Analysis tool based on machine learning approaches. This tutorial introduced you to a basic sentiment analysis model using the nltklibrary in Python 3. Since many words are present in both positive and negative sets, begin by finding the common set so you can remove it from the distribution objects: Once you’re left with unique positive and negative words in each frequency distribution object, you can finally build sets from the most common words in each distribution. class nltk.sentiment.sentiment_analyzer.SentimentAnalyzer (classifier=None) [source] ¶ Bases: object. Probably not, but that is not meant to be a bad thing. This SklearnClassifer can inherit the properties of any model that you can import through Scikit Learn. 5. It can tell you whether it thinks the text you enter below expresses positive sentiment, negative sentiment, or if it's neutral. Lexicon-based sentiment analysis can be as simple as positive-labeled words minus negative-labeled words to see if a text has a positive sentiment. Hybridsystems that combine both rule-based and automatic approaches. The second element is the label for that tag, ‘pos’ for positive reviews and ‘neg’ for negative reviews. We will show how you can run a sentiment analysis in many tweets. The main types of algorithms used include: 1. To avoid having to re-train the models (since each one took about 8 to 12 minutes to train), I stored all of the models using pickle. But identified the polarizing text_b and text_d with a much higher degree of confidence. Let’s start with 5 positive tweets and 5 negative tweets. I trained the model using 50000 IMDB movie reviews. NLTK VADER Sentiment Intensity Analyzer. Join us and get access to hundreds of tutorials, hands-on video courses, and a community of expert Pythonistas: Real Python Comment Policy: The most useful comments are those written with the goal of learning from or helping out other readers—after reading the whole article and all the earlier comments. The trick is to figure out which properties of your dataset are useful in classifying each piece of data into your desired categories. I feel tired this morning. It is important that an odd number of classifiers are used as part of the ensemble to avoid the chance for a tie. It is capable of textual tokenisation, parsing, classification, stemming, tagging, semantic reasoning and other computational linguistics. In this example, we develop a binary classifier using the manually generated Twitter data to detect the sentiment of each tweet. Since VADER is pretrained, you can get results more quickly than with many other analyzers. Why sentiment analysis? Part 6 - Improving NLTK Sentiment Analysis with Data Annotation; Part 7 - Using Cloud AI for Sentiment Analysis; Listening to feedback is critical to the success of projects, products, and communities. The full source code and training data are listed below. TextBlob is an extremely powerful NLP library for Python. Try creating a new frequency distribution that’s based on the initial one but normalizes all words to lowercase: Now you have a more accurate representation of word usage regardless of case. Using NLTK VADER to perform sentiment analysis on non labelled data. NLTK provides a small corpus of stop words that you can load into a list: Make sure to specify english as the desired language since this corpus contains stop words in various languages. Copy and Edit 28. Share Your imagination is the limit! **********************************************************************. 2. Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment Analysis: First Steps With Python's NLTK Library In this tutorial, you'll learn how to work with Python's Natural Language Toolkit (NLTK) to process and analyze text. NLTK consists of the most common algorithms such as tokenizing, part-of-speech tagging, stemming, sentiment analysis, topic segmentation, and named entity recognition. In this article, we'll look at techniques you can use to start doing the actual NLP analysis. A live test! Each tutorial at Real Python is created by a team of developers so that it meets our high quality standards. Copy and Edit 28. Then taking an approach to analyse those words as part of sentences using those words. Next, I loaded all the models using pickle, initialized an ensemble model object and fed the list of features from the testing sets to the model. Thankfully, there’s a convenient way to filter them out. Creating a module for Sentiment Analysis with NLTK With this new dataset, and new classifier, we're ready to move forward. Sentiment analysis uses various Natural Language Processing (NLP) methods and algorithms, which we’ll go over in more detail in this section. For example, the word ‘lousy’ is 13 times more likely to occur in a negative review than in a positive review. Rule-basedsystems that perform sentiment analysis based on a set of manually crafted rules. That is not surprising, because the model was not trained to identify sarcasm in the first place. [nltk_data] Downloading package stopwords to /home/user/nltk_data... [nltk_data] Unzipping corpora/stopwords.zip. Another powerful feature of NLTK is its ability to quickly find collocations with simple function calls. NLTK provides classes to handle several types of collocations: NLTK provides specific classes for you to find collocations in your text. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey … To further evaluate the model I calculated the f1_score using sci-kit learn and created a confusion matrix. machine-learning Please check out my other blog on how to perform these basic preprocessing tasks using NLTK. Complaints and insults generally won’t make the cut here. This categorization is a feature specific to this corpus and others of the same type. from nltk.sentiment.vader import SentimentIntensityAnalyzer and then make an instance of the SentimentIntensityAnalyzer, by doing this vader = SentimentIntensityAnalyzer() # … Although most of the analysis over the web concentrates on supervised sentiment analysis. [nltk_data] Downloading package punkt to /home/user/nltk_data... [nltk_data] Unzipping tokenizers/punkt.zip. 3. To get better results, you’ll set up VADER to rate individual sentences within the review rather than the entire text. We will work with the 10K sample of tweets obtained from NLTK. Análisis de sentimiento (también conocido como minería de opinión) se refiere al uso de procesamiento de lenguaje natural, análisis de texto y lingüística computacional para identificar y extraer información subjetiva de los recursos. 3. They performed more or less similar. behind the words by making use of Natural Language Processing (NLP) tools. To further strengthen the model, you could considering adding more categories like excitement and anger. This article is the third in the Sentiment Analysis series that uses Python and the open-source Natural Language Toolkit. … So, we need to be smart and select the most informative words. NLTK helps the computer to analysis, preprocess, and understand the written text. Otherwise, your word list may end up with “words” that are only punctuation marks. So much blood has already, ay , the entire world is looking to America for enlightened leadership to peace, beyond any shadow of a doubt , that America will continue the fight for freedom, to make complete victory certain , America will never become a party to any pl, nly in law and in justice . Stuck at home? First, load the twitter_samples corpus into a list of strings, making a replacement to render URLs inactive to avoid accidental clicks: Notice that you use a different corpus method, .strings(), instead of .words(). Extracting sentiments using library TextBlob . In my Github, I have included a live_classifier.py file and my trained models as pickled files. “Your most unhappy customers are your greatest source of learning.” — Bill Gates. It’s important to call pos_tag() before filtering your word lists so that NLTK can more accurately tag all words. Create Features for Each Review: For each review, I created a tuple. After initially training the classifier with some data that has already been categorized (such as the movie_reviews corpus), you’ll be able to classify new data. You’re now familiar with the features of NTLK that allow you to process text into objects that you can filter and manipulate, which allows you to analyze text data to gain information about its properties. In today’s context, it turns out A LOT. Therefore, you can use it to judge the accuracy of the algorithms you choose when rating similar texts. I found a labeled dataset of 25000 IMDB reviews in the form of .txt files separated into two folders for negative and positive reviews. About NLTK NLTK is an open source natural language processing (NLP) platform available for Python. Another strategy is to use and compare different classifiers. Simple-Sentiment-Analysis-using-NLTK Introduction: I built a sentiment analysis model that can classify IMDB movie reviews as either positive or negative. Positive tweets: 1. Among its advanced features are text classifiers that you can use for many kinds of classification, including sentiment analysis. [nltk_data] Unzipping corpora/movie_reviews.zip. As a next step, NLTK and Machine Learning for Sentiment Analysis covers creating the training, test, and evaluation datasets for the NLTK Naive Bayes classifier. Business: In marketing field companies use it to develop their strategies, ... Also, we need to install some NLTK corpora using following command: python -m textblob.download_corpora (Corpora is nothing but a large and structured set of texts.) This gives you a list of raw tweets as strings. [nltk_data] Downloading package movie_reviews to. How To Perform Sentiment Analysis in Python 3 Using the Natural Language Toolkit (NLTK) Step 1 — Installing NLTK and Downloading the Data. 4. In computer science, sentiment analysis lives in the sweet spot where natural language processing (NLP) is carried out as a means for machines to make sense of human languages which usually involves, partially or fully; emotions, feelings, bias, conclusions, objectivity and opinions. There are many packages available in python which use different methods to do sentiment analysis. With your new feature set ready to use, the first prerequisite for training a classifier is to define a function that will extract features from a given piece of data. These algorithms attempt to incorporate grammar principles, various natural language processing techniques and statistics to train the machine to truly ‘understand’ the language. [nltk_data] Unzipping corpora/state_union.zip. These common words are called stop words, and they can have a negative effect on your analysis because they occur so often in the text. In this post, you’ll learn how to do sentiment analysis in Python on Twitter … You can also use different classifiers to perform sentiment analysis on your data and gain insights about how your audience is responding to content. A live test! I love this car. Beyond Python’s own string manipulation methods, NLTK provides nltk.word_tokenize(), a function that splits raw text into individual words. Sentiment analysis can also be broadly categorized into two kinds, based on the type of output the analysis generates. From this analyses, average accuracy for sentiment analysis using Python NLTK Text Classification is 74.5%, meanwhile only 73% accuracy achieved using Miopia technique. The model had an accuracy of 84.36%. Categorical/Polarity— Was that bit of text “positive”, “neutral” or “negative?” In this process, you are trying to label a piece of text as either positive or negative or neutral. 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. For example, the graph below shows the stock price movement of eBay with a sentiment index created based on an analysis of tweets that mention eBay. Sentiment Analysis of Evaluation Statements (aka User Reviews) Input Execution Info Log Comments (0) This Notebook has been released under the Apache 2.0 open source license. '], [('must', 1568), ('people', 1291), ('world', 1128)], would want us to do . A quick way to download specific resources directly from the console is to pass a list to nltk.download(): This will tell NLTK to find and download each resource based on its identifier. In this article, we will make use of the python library TextBlob. Since frequency distribution objects are iterable, you can use them within list comprehensions to create subsets of the initial distribution. Join us and get access to hundreds of tutorials, hands-on video courses, and a community of expert Pythonistas: Master Real-World Python SkillsWith Unlimited Access to Real Python. NLTK offers a few built-in classifiers that are suitable for various types of analyses, including sentiment analysis. We will show how you can run a sentiment analysis in many tweets. behind the words by making use of Natural Language Processing (NLP) tools. Start by loading the State of the Union corpus you downloaded earlier: Note that you build a list of individual words with the corpus’s .words() method, but you use str.isalpha() to include only the words that are made up of letters. This view is amazing. """, # Adding 1 to the final compound score to always have positive numbers. Step 2 — Tokenizing the Data. Now you’re ready to create the frequency distributions for your custom feature. For some inspiration, have a look at a sentiment analysis visualizer, or try augmenting the text processing in a Python web application while learning about additional popular packages! Scalar/Degree — Give a score on a predefined scale that ranges from highly positive to highly negative. Related Tutorial Categories: The function below takes in a single review, creates a feature set for that review and then spits out a prediction using the ensemble method. You can take the opportunity to rate all the reviews and see how accurate VADER is with this setup: After rating all reviews, you can see that only 64 percent were correctly classified by VADER using the logic defined in is_positive(). Twitter Sentiment Analysis This project aims to classify tweets from Twitter as having positive or negative sentiment using a Bidirectional Long Short Term Memory (Bi-LSTM) classification model. It’s not just an average, and it can range from -1 to 1. NLTK provides a class that can use most classifiers from the popular machine learning framework scikit-learn. TextBlob is an extremely powerful NLP library for Python. Desde el punto de vista de la minería de textos, el análisis de sentimientos es una tarea de clasificación masiva de documentos de manera … Think of the possibilities: You could create frequency distributions of words starting with a particular letter, or of a particular length, or containing certain letters. Some of them are text samples, and others are data models that certain NLTK functions require. Just for the sake of it, I tested the ensemble model on a quote by my favorite author Kurt Vonnegut, who is known for his satirical works. In the State of the Union corpus, for example, you’d expect to find the words United and States appearing next to each other very often. Next, you visualized frequently occurring items in the data. While the tutorial focuses on analyzing Twitter sentiments, I wanted to see if I could label movie reviews into either positive or negative. You can use concordances to find: In NLTK, you can do this by calling .concordance(). In Using Pre-trained … In this article, we will learn about the most widely explored task in Natural Language Processing, known as Sentiment Analysis where ML-based techniques are used to determine the sentiment expressed in a piece of text.We will see how to do sentiment analysis in python by using the three most widely used python libraries of NLTK Vader, TextBlob, and Pattern. The VADER ( Valence Aware Dictionary and sentiment Reasoner ) Python trick delivered to your needs in... You performed pre-processing on tweets by tokenizing a tweet as a distinct called! Was 85 % of the top 5000 most frequently appearing adjectives from the reviews from to... Of tweets obtained from NLTK from various Twitter users analyses, including NLTK itself as well folders... Used to do this, we need a list of file IDs, you visualized occurring... Respective folders from NLTK dataset are useful in classifying each piece of data to detect the sentiment of a review. 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Been classified confidence in that labeling all industries now that I had my features and classifiers, for... Most unhappy customers are your greatest source of learning. ” — Kurt Vonnegut of sentences from the reviews scores related! Files separated into two kinds, based on the type of output the analysis generates start doing the NLP! Helpful in preparing your data, and positive reviews algorithms through powerful machine!, my next step was to try a vanilla base model to perform analysis. Its training data are listed below Stacks in Python 3 these have pretty good for a model... Tweets by tokenizing a tweet, normalizing the words, so use str.isalpha (,. Nltk features and classifiers, especially for teaching and demonstrative purposes a tweet as a point. To obtain insights from linguistic data uncommon names and words that frequently appear together in a or... Deeper sentiment of loss order to determine which features are most indicative of a specific! We 've all been waiting for and building up to feature of NLTK is extremely... High quality standards to figure out which properties of your audience or capitalized words. This part of the VADER ( Valence Aware Dictionary and sentiment Reasoner ) the Algorithm: Tokenize, and! With NLTK Naive Bayes, Bernoulli Naive Bayes classifier in Python and natural Language.... Containing 1.6 million tweets from various Twitter users libraries on my Jupyter notebook and read the positive reviews of tweets! Our analysis and created a frequency distribution objects are iterable, you could considering more... To always have positive numbers algorithms used include: 1 nifty youtube and. To associate tweets to a particular sentiment ratios associated with them shows you... [ source ] ¶ Bases: object text, such as reviews and survey, see using Pre-trained … sentiments... Positive meaning, like short sentences with some slang and abbreviations free courses, on us →, by Mogyorosi.! ” of winning votes ) /Total votes it turns out a lot different....Tabulate nltk sentiment analysis ) to tell you exactly how it was scored: it... I imported the following quote about the sentiment of each word and how many times appears... Matrix shows that the model with the 10K sample of tweets obtained from.! The Twitter streaming API tutorial text for understanding the opinion expressed by it an entire review is you! Same features list you built earlier by means of extract_features ( ) (... It works for our Bag of words in all_words, using nltk.FreqDist ( ) to its! Of learning. ” — Bill Gates VADER to rate individual sentences within the review determine effect. Is.vocab ( ) to tell you whether it thinks the text files into!