Word Processing Beginner: Fill & Download for Free

What makes Microsoft Word so popular among beginners in word processing?The reasons I can think of are these:Its basic functions are fairly intuitive, so it’s possible to acquire rudimentary proficiency “on your own.”It’s understood to be the “king” of office word processing software, so beginners know that their experience with it will be valuable.It’s the one beginners have “heard of,” so they seek it out.The trial version came installed on the computer they just bought.It’s the one they know they’re going to have to use in the job they are seeking or just got, so now’s the time.HTH. HAND.

Look long enough, . . .. . . and you will see patterns.The real trick is to make sure you are appropriately skeptical of the patterns you find.That's what separates the paranoid conspiracy theorist from the scientist. The conspiracy theorist assumes every pattern is meaningful. The scientist knows many patterns will arise by chance or relate only surface-level features. She will rigorously test the patterns she finds before concluding with confidence that there's a deeper connection.With that said, . . .. . . the more expertise you have in a particular field, the more likely you will be to see meaningful patterns in events that relate to that field.The chess grandmaster will typically see more meaningful patterns on the board than will the novice. But the grandmaster will ALSO usually engage in careful calculation before acting on these patterns.So part of the answer is that you should immerse yourself in the domain in which you want to make connections. Learn the main concepts, distinctions, relationships and so forth. And practice looking at things through the special lenses the field provides, until you start naturally seeing patterns you couldn't see as a rank beginner.And with that also said, . . .. . . there are other tricks to getting raw data to give up their secrets.With some data you can use statistical tricks and other formal methods to find correlations of various levels.Less formally, a trick I use all the time is to open a fresh word processing document and just start asking and answering questions about the data.Ask a question that seems relevant and make a bullet list below it. List possible answers until either you're dry or you think of a more interesting question to ask. Then ask another question. And just keep going until you run out of questions.I use this method for conceptual analysis and exploration. In this case the data (or "events") are the things people say. And the patterns I'm looking for are potential (fuzzy) boundaries for the concepts, and potentially useful relationships among concepts.Almost every time I do this exercise I make connections I didn't see before I started.And then it's time to test those patterns to see if they hold up with new data.

Let me explain the relation between Sentiment analysis, Machine learning, NLP using very simple imageMachine Learning and NLP are two children of AI, which works together and helps to solve many data problems.Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.Sentiment Analysis (Formal definition) is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral.There are mainly three approaches for performing the sentiment analysis (Pathan Karimkhan's answer to How does sentiment analysis work, generally?)Lexicon based - considers lexicon dictionary for identifying polarity of the text (Purely NLP based)Machine learning based approach - Needs to develop classification model, which is trained using prelabeled dataset of positive, negative, neutral content. (Combines concept of NLP and ML)Combined approach - Which uses lexicon dictionary along with pre-labelled data set for developing classification model. (Combines approach 1 and approach 2)Now let me explain with the steps of sentiment analysis. You will get idea how NLP and ML used for the game. I have provided very basic steps one can drill deeper for better accuracy. (Pathan Karimkhan's answer to I am a beginner in this field and want to do sentiment analysis on a given data set. Where is a step-by-step tutorial for the same?)Get terms - Reduce each review(text) to the list of words (Using NLP Concepts of tokenization and ngraming)Filtering - Remove unnecessary words that will not add value for sentiment analysis – is, among, but, and, it, that (NLP concepts of Stop words processing)BaseWord - Convert all inflections to their root word - fry, fries, fried -> fry (NLP Concept of stemming and lemmatization)Negation detection - Identifying the negation context i.e not happy -> sad, not bad-> good (Again using NLP concepts of token processing)Feature generations - Use the words thus extracted from a review as features to indicate the positiveness or negativeness of that review (Use feature extraction algorithms Bag of word, TF-IDF, Count vectorizer etc which - NLP again helps here)Statistical Classifier - Train a machine learning classifier to predict positivity (Applying Machine Learning algorithms on features generated in previous steps to predict the polarity of the document)So NLP helps machine to understand the text, from this understanding ML algorithms enables program to learn from historical text documents to predict the negative-positive-neutral polarity.