Edmond  Herzog

Edmond Herzog

1596537540

Mood & Modality and Dialogue Sentiment

In this article, we will see how verbal functional categories used in customer dialogue text and how these categories contribute to the semantics, especially the text sentiment.

Verb phrase in a sentence sometimes can carry huge semantics, sometimes hint the sentiment only by itself even though if one does not see the rest of the context words, hence contribute to sentiment analysis models as important features. For example, cross all the not-included-in-any-VP words from the following customer reviews:

The product isn't working properly. 
I didn't like this product.
I'm not satisfied with the product quality at all.

In order to charge meaning to the sentence, many languages like the verb to admit different inflections such as tense and person. Moreover most of the time we want to express our feeling and opinions about how the proposed action by the verb happened: are we sure, did we see the action by our own eyes, do we think it’s likely or unlikely?

This is more of a semantic capability, thus one usually needs more than inflecting the verb, more than grammatical constructions._ Function of the verb_ is a broad topic, but I will explain some basic concepts before the statistical parts. You can skip to the next section if you have this background.

Tense is a grammatical realization of time by means of verbal inflection. English has 2 tenses: past and presentFuture is not a tense not having an inflectional marker, but it is rather a time . Future time is formed either by will or with adverbs such as tomorrow , 8 o'clock or next week/month etc. As you see tense is a grammatical concept and time is rather a semantic concept.

Another concept is aspect, a grammatical category which reflects the action given by the verb happened with respect to time. English has two aspects:

action complete:    perfective      has moved, had moved
action in progress: progressive     is moving, was moving

#nlp #ai #computational-linguistics #lstm #spacy

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Mood & Modality and Dialogue Sentiment
CodingNepal .

CodingNepal .

1617797839

Popup Share Modal UI Design using HTML CSS & JavaScript

#css share modal #modal dialog box #popup share modal #share modal #share modal in javascript

Einar  Hintz

Einar Hintz

1602560783

jQuery Ajax CRUD in ASP.NET Core MVC with Modal Popup

In this article, we’ll discuss how to use jQuery Ajax for ASP.NET Core MVC CRUD Operations using Bootstrap Modal. With jQuery Ajax, we can make HTTP request to controller action methods without reloading the entire page, like a single page application.

To demonstrate CRUD operations – insert, update, delete and retrieve, the project will be dealing with details of a normal bank transaction. GitHub repository for this demo project : https://bit.ly/33KTJAu.

Sub-topics discussed :

  • Form design for insert and update operation.
  • Display forms in modal popup dialog.
  • Form post using jQuery Ajax.
  • Implement MVC CRUD operations with jQuery Ajax.
  • Loading spinner in .NET Core MVC.
  • Prevent direct access to MVC action method.

Create ASP.NET Core MVC Project

In Visual Studio 2019, Go to File > New > Project (Ctrl + Shift + N).

From new project window, Select Asp.Net Core Web Application_._

Image showing how to create ASP.NET Core Web API project in Visual Studio.

Once you provide the project name and location. Select Web Application(Model-View-Controller) and uncheck HTTPS Configuration. Above steps will create a brand new ASP.NET Core MVC project.

Showing project template selection for .NET Core MVC.

Setup a Database

Let’s create a database for this application using Entity Framework Core. For that we’ve to install corresponding NuGet Packages. Right click on project from solution explorer, select Manage NuGet Packages_,_ From browse tab, install following 3 packages.

Showing list of NuGet Packages for Entity Framework Core

Now let’s define DB model class file – /Models/TransactionModel.cs.

public class TransactionModel
{
    [Key]
    public int TransactionId { get; set; }

    [Column(TypeName ="nvarchar(12)")]
    [DisplayName("Account Number")]
    [Required(ErrorMessage ="This Field is required.")]
    [MaxLength(12,ErrorMessage ="Maximum 12 characters only")]
    public string AccountNumber { get; set; }

    [Column(TypeName ="nvarchar(100)")]
    [DisplayName("Beneficiary Name")]
    [Required(ErrorMessage = "This Field is required.")]
    public string BeneficiaryName { get; set; }

    [Column(TypeName ="nvarchar(100)")]
    [DisplayName("Bank Name")]
    [Required(ErrorMessage = "This Field is required.")]
    public string BankName { get; set; }

    [Column(TypeName ="nvarchar(11)")]
    [DisplayName("SWIFT Code")]
    [Required(ErrorMessage = "This Field is required.")]
    [MaxLength(11)]
    public string SWIFTCode { get; set; }

    [DisplayName("Amount")]
    [Required(ErrorMessage = "This Field is required.")]
    public int Amount { get; set; }

    [DisplayFormat(DataFormatString = "{0:MM/dd/yyyy}")]
    public DateTime Date { get; set; }
}

C#Copy

Here we’ve defined model properties for the transaction with proper validation. Now let’s define  DbContextclass for EF Core.

#asp.net core article #asp.net core #add loading spinner in asp.net core #asp.net core crud without reloading #asp.net core jquery ajax form #asp.net core modal dialog #asp.net core mvc crud using jquery ajax #asp.net core mvc with jquery and ajax #asp.net core popup window #bootstrap modal popup in asp.net core mvc. bootstrap modal popup in asp.net core #delete and viewall in asp.net core #jquery ajax - insert #jquery ajax form post #modal popup dialog in asp.net core #no direct access action method #update #validation in modal popup

Sofia  Maggio

Sofia Maggio

1626077565

Sentiment Analysis in Python using Machine Learning

Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?

We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.

But why do we need sentiment analysis?

Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.

It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.

Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.

Lastly, they are also used for spam filtering and removing unwanted content.

How does sentiment analysis work?

NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.

NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.

Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.

Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are

  • Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
  • Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
  • The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc

Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.

  • Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
  • This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
  • Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.

In this machine learning project, we will use recurrent neural network for sentiment analysis in python.

#machine learning tutorials #machine learning project #machine learning sentiment analysis #python sentiment analysis #sentiment analysis

Edmond  Herzog

Edmond Herzog

1596537540

Mood & Modality and Dialogue Sentiment

In this article, we will see how verbal functional categories used in customer dialogue text and how these categories contribute to the semantics, especially the text sentiment.

Verb phrase in a sentence sometimes can carry huge semantics, sometimes hint the sentiment only by itself even though if one does not see the rest of the context words, hence contribute to sentiment analysis models as important features. For example, cross all the not-included-in-any-VP words from the following customer reviews:

The product isn't working properly. 
I didn't like this product.
I'm not satisfied with the product quality at all.

In order to charge meaning to the sentence, many languages like the verb to admit different inflections such as tense and person. Moreover most of the time we want to express our feeling and opinions about how the proposed action by the verb happened: are we sure, did we see the action by our own eyes, do we think it’s likely or unlikely?

This is more of a semantic capability, thus one usually needs more than inflecting the verb, more than grammatical constructions._ Function of the verb_ is a broad topic, but I will explain some basic concepts before the statistical parts. You can skip to the next section if you have this background.

Tense is a grammatical realization of time by means of verbal inflection. English has 2 tenses: past and presentFuture is not a tense not having an inflectional marker, but it is rather a time . Future time is formed either by will or with adverbs such as tomorrow , 8 o'clock or next week/month etc. As you see tense is a grammatical concept and time is rather a semantic concept.

Another concept is aspect, a grammatical category which reflects the action given by the verb happened with respect to time. English has two aspects:

action complete:    perfective      has moved, had moved
action in progress: progressive     is moving, was moving

#nlp #ai #computational-linguistics #lstm #spacy

Anshu  Banga

Anshu Banga

1602824181

How to Calculate a Sentiment Score for Words in Python

For this example, we will use a Twitter dataset that comes with NLTK. This dataset has been manually annotated and serves to establish baselines for models quickly. The sample dataset from NLTK is separated into positive and negative tweets. It contains 5000 positive tweets and 5000 negative tweets exactly. The exact match between these classes is not a coincidence. The intention is to have a balanced dataset. That does not reflect the real distributions of positive and negative classes in live Twitter streams. It is just because balanced datasets simplify the design of most computational methods that are required for sentiment analysis. However, it is better to be aware that this balance of classes is artificial. Let us import them now as well as a few other libraries we will be using.

import nltk                                ## Python library for NLP
from nltk.corpus import twitter_samples    ## sample Twitter dataset from NLTK
from collections import Counter

nltk.download('twitter_samples')

## select the set of positive and negative tweets
all_positive_tweets = twitter_samples.strings('positive_tweets.json')
all_negative_tweets = twitter_samples.strings('negative_tweets.json')

all_positive_tweets[0:10]

and we get:

['#FollowFriday @France_Inte @PKuchly57 @Milipol_Paris for being top engaged members in my community this week :)',
 '@Lamb2ja Hey James! How odd :/ Please call our Contact Centre on 02392441234 and we will be able to assist you :) Many thanks!',
 '@DespiteOfficial we had a listen last night :) As You Bleed is an amazing track. When are you in Scotland?!',
 '@97sides CONGRATS :)',
 'yeaaaah yippppy!!!  my accnt verified rqst has succeed got a blue tick mark on my fb profile :) in 15 days',
 '@BhaktisBanter @PallaviRuhail This one is irresistible :)\n#FlipkartFashionFriday http://t.co/EbZ0L2VENM',
 "We don't like to keep our lovely customers waiting for long! We hope you enjoy! Happy Friday! - LWWF :) https://t.co/smyYriipxI",
 '@Impatientraider On second thought, there’s just not enough time for a DD :) But new shorts entering system. Sheep must be buying.',
 'Jgh , but we have to go to Bayan :D bye',
 'As an act of mischievousness, am calling the ETL layer of our in-house warehousing app Katamari.\n\nWell… as the name implies :p.']

Sentiment Score

We will iterate over reviews. For each word in a positive review, we will increase the count for that word in both our positive counter and the total words counter; likewise, for each word in a negative review, we will increase the count for that word in both our negative counter and the total words counter.

I found the Counter class to be useful in this task.

## Create three Counter objects to store positive, negative and total counts
positive_counts = Counter()
negative_counts = Counter()
total_counts = Counter()

for i in range(len(all_positive_tweets)):
    for word in all_positive_tweets[i].lower().split(" "):
        positive_counts[word]+=1
        total_counts[word]+=1

for i in range(len(all_negative_tweets)):
    for word in all_negative_tweets[i].lower().split(" "):
        negative_counts[word]+=1
        total_counts[word]+=1

Let’s have a look at the most common **positive **and **negative **words:

positive_counts.most_common()[0:10]

And we get:

[(':)', 3154),
 ('you', 1316),
 ('to', 1081),
 ('the', 1076),
 ('i', 1042),
 ('a', 920),
 ('for', 769),
 ('and', 688),
 (':-)', 615),
 (':d', 609)]

And fro the negative:

negative_counts.most_common()[0:10]

And we get:

[(':(', 3723),
 ('i', 2093),
 ('to', 1090),
 ('the', 915),
 ('my', 738),
 ('you', 665),
 ('and', 660),
 ('a', 650),
 ('me', 627),
 ('so', 571)]

As you can see, common words like “ the”, “ a”, “ i” appear very often in both positive and negative reviews. Instead of finding the most common words in positive or negative reviews, what you really want are the words found in positive reviews more often than in negative reviews, and vice versa. To accomplish this, you’ll need to calculate the ratios of word usage between positive and negative reviews.

Positive Over Negative Ratio for the Sentiment Score

pos_neg_ratios = Counter()

## Calculate the ratios of positive and negative uses of the most common words
## Consider words to be "common" if they've been used at least 100 times
for term,cnt in list(total_counts.most_common()):
    if(cnt > 100):
        pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)
        pos_neg_ratios[term] = pos_neg_ratio

Positive Sentiment Score

Let’s have a look at the top 20 words with the highest Positive Sentiment Score:

pos_neg_ratios.most_common()[0:20]

And we get:

[(':)', 3154.0),
 (':-)', 615.0),
 (':d', 609.0),
 (':p', 128.0),
 (':))', 108.0),
 ('thanks', 15.818181818181818),
 ('great', 8.941176470588236),
 ('thank', 8.25),
 ('happy', 7.12),
 ('hi', 6.045454545454546),
 ('<3', 5.291666666666667),
 ('nice', 4.7894736842105265),
 ('!', 4.7727272727272725),
 ('our', 2.78),
 ('new', 2.75),
 ('an', 2.659090909090909),
 ('follow', 2.6481481481481484),
 ('us', 2.6052631578947367),
 ('your', 2.470149253731343),
 ('good', 2.465909090909091)]

Negative Sentiment Score

Let’s have a look at the top 20 words with the highest Negative Sentiment Score:

pos_neg_ratios.most_common()[::-1][0:20]

And we get:

[(':(((', 0.0),
 (':((', 0.0),
 (':-(', 0.0),
 (':(', 0.0002685284640171858),
 ('sad', 0.03773584905660377),
 ('miss', 0.0759493670886076),
 ('followed', 0.11711711711711711),
 ('sorry', 0.12030075187969924),
 ('why', 0.17834394904458598),
 ('wish', 0.19540229885057472),
 ("can't", 0.23863636363636365),
 ('feel', 0.2857142857142857),
 ('wanna', 0.29473684210526313),
 ('want', 0.33796296296296297),
 ('please', 0.35),
 ('been', 0.3770491803278688),
 ('still', 0.3884297520661157),
 ('but', 0.4028436018957346),
 ('im', 0.421875),
 ('too', 0.4221105527638191)]

Conclusion

As we can see got expected results and more specifically:

  • The “emojis” are the most powerful tokens. This is another reason why we should be careful when removing punctuations in Sentiment Analysis and in NLP tasks
  • The Top 5 Positive tokens where related to smile face like :):-) , :D:P :)) . Notice that we have converted all the letters to lower case.
  • Other positive words that we found were the thanksgreathappy and nice,
  • The Top 4 Negative tokens were related to sad faces like :(((:((:-( and :(

Originally published at https://predictivehacks.com.

#nlp #python #nltk #sentiment-analysis #sentiment