投稿時間:2023-03-25 22:18:41 RSSフィード2023-03-25 22:00 分まとめ(20件)

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AWS	lambdaタグが付けられた新着投稿 - Qiita	S3のオブジェクトを更新したらLambdaレイヤーの更新も必要？	https://qiita.com/ShinyaNakayama/items/65739a7341be720ecb6f	ffmpeg	2023-03-25 21:33:14
python	Pythonタグが付けられた新着投稿 - Qiita	PythonのTkinterでFrameをスクロールする	https://qiita.com/thruaxle/items/0fc3e89bd15e3d2f79a1	frame	2023-03-25 21:58:31
python	Pythonタグが付けられた新着投稿 - Qiita	S3のオブジェクトを更新したらLambdaレイヤーの更新も必要？	https://qiita.com/ShinyaNakayama/items/65739a7341be720ecb6f	ffmpeg	2023-03-25 21:33:14
js	JavaScriptタグが付けられた新着投稿 - Qiita	GPT-4で、JSブロックくずしをステップバイステップで作ってみた	https://qiita.com/y_catch/items/fd4268af8e4e8976547f	chatgpt	2023-03-25 21:51:41
Ruby	Rubyタグが付けられた新着投稿 - Qiita	3/25 Ruby入門remained クラスなどなど編	https://qiita.com/TeihenEngineer/items/8c000f6e10c6ee5d739b	nameenddefsayhiputs	2023-03-25 21:52:37
AWS	AWSタグが付けられた新着投稿 - Qiita	S3のオブジェクトを更新したらLambdaレイヤーの更新も必要？	https://qiita.com/ShinyaNakayama/items/65739a7341be720ecb6f	ffmpeg	2023-03-25 21:33:14
Docker	dockerタグが付けられた新着投稿 - Qiita	MintにDockerをインストールしたいときに要注意	https://qiita.com/LabPixel/items/524913afead1897366b0	aptupdatelt	2023-03-25 21:41:56
Git	Gitタグが付けられた新着投稿 - Qiita	ローカルリポジトリのコミットから、任意のファイルを抽出する	https://qiita.com/QiitaYkuyo/items/e48b89fe2162c8941c4b	archive	2023-03-25 21:37:43
海外TECH	MakeUseOf	5 Online Tools and Websites to Make Your Own Emojis	https://www.makeuseof.com/make-own-emojis-online-tools/	online	2023-03-25 12:30:16
海外TECH	MakeUseOf	What Is a Virtual Tabletop (VTT) and How Does it Work?	https://www.makeuseof.com/what-is-virtual-tabletop-how-it-works/	tabletop	2023-03-25 12:01:16
海外TECH	DEV Community	Sentiment Analysis	https://dev.to/mburu_elvis/sentiment-analysis-548	Sentiment Analysis Getting Started With Sentiment AnalysisIt is the process of detecting positive or negative sentiment in text It is also referred to as opinion mining It is an approach to natural language processing NLP that identifies the emotional tone behind a body of text It is vastly used by organizations to determine and categorize opinions about a produt service or ideaSentiment analysis involves the use of data mining machine learning ML artificial intelligenceand computational linguistics to mine text for sentiment and subjective information Such information maybe classified as positiveneutralnegativeThis classification is also known as polarity of a text Graded Sentiment Analysisvery positivepositiveNeutralNegativeVery NegativeThis is also referred to as graded or fine grained sentiment anlysis Types of Sentiment AnalysisIntent based recognizes motivation behind a textFine grained graded sentiment analysisEmotion detection allows detection of various emotionsAspect based anayses text to know particular aspects features mentioned in all the polarity We will not dive into these types for now This in turn helps organizations to gather insights into real time customer sentiment customer experience and brand reputation Generally these tools use text analytics to analyze online sources Benefits of sentiment analysissorting data as scalereal time analysisconsistent criteria Steps involved in Sentiment AnalysisSentiment analysis generally follows the following steps Collect data The text to be analyzed is identified and collected Clean the data The data is processed and cleaned to remove noise and parts of speech that don t have meaning relevant to the sentiment of the text Extract features A machine learning algorithm automatically extracts text features to identify negative or positive sentiment Pick an ML model A sentiment analysis tool scores the text using rule based automatic or hybrid ML model Sentiment classification Once a model is picked an used to analyze a piece of text it assigns a sentiment score to the text including positive negative of neutral Let s have a deep dive in sentiment analysis using an example Step Collect DataWe are going to used a data set from UCI Machine Learning Repository Let s start with importing the libraries that we will be using punkt is a data package that contains pre trained models for tokenization import the required packages and librariesimport numpy as npimport pandas as pdimport nltknltk download punkt loading the datasetpd set option display max colwith None df pd read csv df head OutputWe can now see that there are only two columns text and label The label indicates the sentiment of the review indicates a postive sentiment indicates a negative sentiment This thus indicates the polarity of the sentiment We now create a sample string which is the first entry in the text column of the dataframe df sample df text sampleOutput Tokens and Bigrams a TokensA token is a single unit of meaning that can be identified in a text It is also known as a unigram Tokenization is the process of breaking down a text into individual tokens The functions that perform tokenization are called tokenizers This concept is implemented with the nltk word tokenize function the function takes a string of text as input and returns a list of tokens it splits the text into individual words and punctuation marks Let s see an example the functions usage by tokenizing the sample text sample tokens nltk tokenize sample sample tokens view a list of elements upto the th tokenOutput b BigramsIf we combine two unigrams tokens we form a bigram A bigram is a pair of adjecent tokens in a text They are used to capture some of the context in which a particular word or phrase appers They are used to build statistical models of language which are sequences of n words tokens By analyzing the frequency of different n grams in a large corpus of text NLP systems can learn to predict the probability of dofferen words occuring in a particular context bigrams are implememted with the nltk bigrams functionLet s see this in actionsample bitokes list nltk bigrams sample tokens Return the first bigramssample bitokens Output Frequency DistributionRefers to the count or proportion of words or prases asscociated with positive or negative sentiment It basically counts the occurrence of each sentiment bearing word phrase and then calculate the frequency distribution implemented using the nltk FreqDist functionWhat are the top most frequently used tokens in our sample sample freqdist nltk FreqDist sample tokens Return the top most frequent tokenssample freqdist most common OutputThis results ultimately make sense a comma the a or periods can be quite common in a phrase Let s create a function named tokens top that takes in a text as input and returns the top n most common tokens in a given text def tokens top text n create tokens tokens nltk word tokenize text create the frequency distribution freqdist nltk FreqDist tokens return the top n most common tokens return freqdist most common n Call the function tokens top df text Output Document Term MatrixIt is a matrix that represents the frequency of terms that occur in a collection of documents The rows represent the documents in the corpus and the columns represent the terms The cells of the matrix represents the frequency or weight of each term We can implement this with scikit learn s CountVectorizerExample import the packagefrom sklearn feature extraction text import CountVectorizerdef create dtm series Create an instance object of the class cv CountVectorizer create a dtm from the series parameter dtm cv fit transform series convert the sparse array to a dense array dtm dtm todense get column names features cv get feature names out create a dataframe dtm df pd DataFrame dtm columns features return the dataframe return dtm df Call the function for df text headcreate dtm df text head Output Data Cleaning Feature ImportanceRefers to the extent to which a specific feature variable contributes to the prediction or classification in sentiment analysis There are differet methods that can be used to determine feature importance machine learning algorithms eg decision trees and random forestsstatistical methods eg correlation or regression analysisfeature importance is a useful tool in sentiment analysis as it can help identify the most important features for accurately predicting the sentiment of a text Examplewe ll define a function top n tokens that has parameterstext sentiment and nthe function will return the top n most important tokensto predict the sentiment of the text We ll use LogisticRegression from sklearn linear modelwith the following parameters solver lbfgs max iter random state from sklearn linear model import LogisticRegressiondef top n tokens text sentiment n create an instance of the class lgr LogisticRegression solver lbfgs max iter random state cv CountVectorizer create the DTMdtm cv fit transform text fit the logistic regression modellgr fit dtm sentiment get the coefficientscoefs lgr coef create the features column namesfeatures cv get features names out create the dataframedf pd DataFrame Tokens features Coefficients coefs return the largest nreturn df nlargest n coefficients Test if on df text top n tokens df text df label OutputTo validate the hypothesis that the most important features will be the ones that indicate a strong positive sentiment let s look at the smallest coefficients from sklearn linear model import LosticRegressiondef bottom n tokens text sentiment n create an instance of the class lgr LogisticRegression solver lbfgs max iter random state cv CountVectorizer create the DTM dtm cv fit transform text fit the logistic regression model lgr fit dtm sentiment get the coefficients coefs lgr coef create the features column names features cv get features names out create the dataframe df pd DataFrame Tokens features Coefficients coefs return the smallest n return df nmallest n coefficients Test if on df text bottom n tokens df text df label OutputIn the example that we ve covered till this far we ve used labelled data What if we do not have labelled data Then we can use pre trained models such as TextBlob VADERStanford ColeNLPGoogle Cloud Natural Language APIHugging Face TransformersLet s explore TextBlob TextBlobIt is a Python library that provides a simple API for performing common NLP tasks such as sentiment analysis It uses a pre trained model to assign a sentiment score to a piece of text ranging from to It is built on top of NLTK natural language toolkit It also provides additional information such as subjectivity scoreIt returns the sentiment of agiveen data in the format of a named tuple as follows polarity subjectivity polarity score is a float within the range of it aims at differentiating whether the text is positive or negativesubjectivity is a float within the range is very objective is very subjectiveTextBlob also provides other features such as part of speech tagging a noun phrase extraction ExampleLet s define a function named polarity subjectivity that accepts two argument The function uses TextBlob to the provided textif print results True prints polarity and subjectivity of the text elseM returns a tuple of float values st being polarity and nd being subjectivityYou can install TextBlob using pip install textblob import TextBlobfrom textblob import TextBlobdef polarity subjectivity text sample print results False create an instance of TextBlob tb TextBlob text if condition is metm print the results if print results print f Polarity is round tb sentiment Subjectivity round tb sentiment else return tb sentiment tb sentiment Test the functionpolarity subjectivity sample print results True OutputThe results indicate that our sample has a slight positive polarity and it s relatively subjective thought not by a high degreeLet s define a function token count that accepts a string and using nltk s word tokenizer returns an integer number of tokens in the given stringThen define another function series tokens that accepts a Pandas Series as argument and aplies the functiontoken count to the given series Use the second function on the top rows of our dataframe import librariesfrom nltk import word tokenize Define the first function that counts the number of tokens in a given stringdef token count string return len word tokenize string Define the second function that applies the token count funnction to a given Pandas seriesdef series tokens series return series apply token count Apply the function to the top rows of the data frameseries tokens df text head OutputLet s define a function named series polarity subjectivitythat applies the polarity subjectivity function we defined earlier define the functiondef series polarity subjectivity series return series apply polarity subjectivity apply to the top rows of df text series polarity subjectivity df text head Output Measure of Complexity Lexical DiversityLexical diversity refers to the variety of words used in a piece of writing or speech It is a measure of how often different words are used in a given text or speech and is often used as an indicator of the richnes and complexity of vocabulary It thus defines the number of unique tokens over the total number of tokens ExampleLet s define a complexity function that accepts a string as an argument and returns the lexical complexity score defined as the number of unique tokens over the total number of tokens def complexity string create a list of all tokens total tokens nltk word tokenize string create a set of words It keeps only unique values unique tokens set total tokens Return the complexity measure if len total tokens gt return len unique tokens len total tokens apply the function to top rowsdf text head apply complexity OutputSome interesting insights the row at index and have the highest lexical diversity All the tokens in them are totally unique Text Cleanup Stopwords and Non alphabeticalsThis step ensures that the text data is in a constitent format and to remove noise irrelevant information and other inconsitencies Some of the techniques for text cleanup LowercasingTokenizationStopword RemovalRemoving PunctuationStemming and LemmatizationRemoving URL s and mentionsRemoving emojis and emotions Example import the libraryfrom nltk corpus imort stopwords Select only English stopwordsenglish stop words stopwords words english print the first print english stop words Let s look at an example to remove non alphabeticalWe ll use isalphastring Crite Jes cd string a quick dog string We are good print f String string isalpha n print f String string isalpha n print f String string isalpha n Output	2023-03-25 12:23:26
海外TECH	DEV Community	12- Map Printing with PyQGIS	https://dev.to/azad77/12-map-printing-with-pyqgis-4hpe	Map Printing with PyQGISLearn how to easily print maps using PyQGIS with this step by step guide PyQGIS is a Python library that provides access to QGIS API allowing you to automate tasks and create custom plugins In this tutorial we ll use QgsLayoutExporter class to export layouts as images PDFs and SVGs Whether you re a GIS professional or a Python developer you ll find this tutorial helpful to enhance your mapping skills We use the QgsLayoutExporter class to export a layout that we opened manager QgsProject instance layoutManager print manager printLayouts layout manager layoutByName Layout We use the QgsLayoutExporter class to export to image SVG and PDF exporter QgsLayoutExporter layout Export to PNG image exporter exportToImage D Python QGIS Layout png QgsLayoutExporter ImageExportSettings Export to PDF exporter exportToPdf D Python QGIS Layout pdf QgsLayoutExporter PdfExportSettings Export to SVG image exporter exportToSvg D Python QGIS Layout svg QgsLayoutExporter SvgExportSettings Use a for loop to export the layout for layout in manager printLayouts exporter QgsLayoutExporter layout exporter exportToImage D Python QGIS Image png format layout name QgsLayoutExporter ImageExportSettings In conclusion this tutorial provides a step by step guide on how to easily print maps using PyQGIS It explains how to use QgsLayoutExporter class to export layouts as images PDFs and SVGs By following this tutorial GIS professionals and Python developers can enhance their mapping skills and automate tasks Overall this tutorial is a helpful resource for anyone looking to improve their knowledge of map printing with PyQGIS If you like the content please SUBSCRIBE to my channel for the future content	2023-03-25 12:05:59
海外TECH	DEV Community	Seaborn - Unlock the Power of Data Visualization	https://dev.to/debjotyms/seaborn-unlock-the-power-of-data-visualization-2lk7	Seaborn Unlock the Power of Data Visualization What is Seaborn Seaborn is a matplotlib based Python data visualization library It provides a sophisticated interface for creating visually appealing and instructive statistical visuals It has beautiful default styles and it is also designed to work very well with Pandas data frame objects InstallationTo install Seaborn on your system you have to run this code on your command line gt gt pip install seaborn ImportingTo use seaborn in our code first we have to import it We will import the seaborn module under the name sns the tidy way DataNow where can we find our data to visualize The good thing about Seaborn is that it comes with built in data sets Let s talk about some plots that help us see how a set of data is spread out These plots are displotNow the displot is showing our data in histogram form We only need to pass a single column from our data frame to displot jointplotjointplot lets you match up two scatterplots for two sets of data by letting you choose wh ich parameter to compare “scatter “reg “resid “kde “hex Here are different kinds of plots we can create by changing the value of the kind attribute pairplotpairplot will plot pairwise relationships across an entire data frame for the numerical columns and supports a color hue argument for categorical columns We just have to pass the data through the method kdeplotkdeplots are Kernel Density Estimation plots These KDE plots replace every single observation with a Gaussian Normal distribution centered around that value Categorical Data Plots B arplot and Countplot B arplot These extremely similar plots enable the extraction of aggregate data from a categorical feature in the data The barplot is an all purpose plot that aggregates categorical data based on a function by default the mean We can use an estimator attribute to change the default Here we are using standard deviation CountplotThis is essentially the same as a barplot except the estimator is explicitly counting the number of occurrences Which is why we only pass the x value B oxplot and Violinplot Boxplots and violinplots are two types of graphs that can be used to demonstrate the distribution of categorical data A box plot also known as a box and whisker plot is a type of graph that displays the distribution of quantitative data in a manner that makes it easier to make comparisons between different variables or between different levels of a categorical variable The whiskers extend to show the rest of the distribution with the exception of points that are determined to be outliers using a method that is a function of the interquartile range The box displays the quartiles of the dataset while the whiskers show the rest of the distribution B oxplot ViolinplotA violin plot plays a similar role as a box and whisker plot It shows the distribution of quantitative data across several levels of one or more categorical variables such that those distributions can be compared Unlike a box plot in which all of the plot components correspond to actual data points the violin plot features a kernel density estimation of the underlying distribution	2023-03-25 12:05:22
Apple	AppleInsider - Frontpage News	Downgrading from iPhone 13 Pro Max to the iPhone SE 3 is a mixed bag	https://appleinsider.com/articles/23/03/25/downgrading-from-iphone-13-pro-max-to-the-iphone-se-3-is-a-mixed-bag?utm_medium=rss	Downgrading from iPhone Pro Max to the iPhone SE is a mixed bagMillions of people are experimenting with ways to save a few bucks here and changing iPhone can be a good option for some This is what happened when one AppleInsider writer downgraded from their iPhone Pro Max to the iPhone SE iPhone Pro Max takes on the iPhone SE We went from Apple s flagship iPhone model to the entry level budget device to save big on our phone bill Now that we ve had time to adjust to using a different iPhone we noticed several things that may sway other users looking to make a similar cut Read more	2023-03-25 12:56:27
Apple	AppleInsider - Frontpage News	Daily deals March 25: $699 M2 Mac mini, $99 Kindle Paperwhite, $1,000 off LG 77-inch OLED 4K Smart TV, more	https://appleinsider.com/articles/23/03/25/daily-deals-march-25-699-m2-mac-mini-99-kindle-paperwhite-1000-off-lg-77-inch-oled-4k-smart-tv-more?utm_medium=rss	Daily deals March M Mac mini Kindle Paperwhite off LG inch OLED K Smart TV moreTop deals for March include off a inch Samsung K Smart Monitor a Apple MagSafe Charger a AirTag pack and more Get off a inch LG OLED smart TVThe AppleInsider staff scours the internet for can t miss deals at online stores to curate a list of stellar deals on popular tech gadgets including discounts on Apple products TVs accessories and other items We share our top finds in our Daily Deals list to help you save money Read more	2023-03-25 12:05:20
海外TECH	CodeProject Latest Articles	Oasis.MicroService: A Small Package that Simplifies Microservice Deployment	https://www.codeproject.com/Tips/5356844/Oasis-MicroService-A-Small-Package-that-Simplifies	class	2023-03-25 12:39:00
海外ニュース	Japan Times latest articles	Shoma Uno retains men’s world title as Ilia Malinin lands quad axel	https://www.japantimes.co.jp/sports/2023/03/25/figure-skating/isu-worlds-mens-free-uno-malinin/	Shoma Uno retains men s world title as Ilia Malinin lands quad axelThe Nagoya native held off a spirited challenge from an exciting final group at Saitama Super Arena giving Japan world titles in three disciplines for	2023-03-25 21:44:07
ニュース	BBC News - Home	Tornado kills 23 and brings devastation to Mississippi	https://www.bbc.co.uk/news/world-us-canada-65075276?at_medium=RSS&at_campaign=KARANGA	state	2023-03-25 12:28:41
ニュース	BBC News - Home	Ukraine war: Battle for Bakhmut 'stabilising', says commander	https://www.bbc.co.uk/news/world-europe-65072173?at_medium=RSS&at_campaign=KARANGA	ukrainian	2023-03-25 12:44:42
ニュース	BBC News - Home	Jonny Bairstow: England batter to miss IPL to continue injury rehab with Yorkshire	https://www.bbc.co.uk/sport/cricket/65038301?at_medium=RSS&at_campaign=KARANGA	Jonny Bairstow England batter to miss IPL to continue injury rehab with YorkshireEngland batter Jonny Bairstow opts to miss the Indian Premier League IPL to continue his recovery from injury before the Ashes in June	2023-03-25 12:24:37