#opinion mining

[xcelglobalpanel]

Unpacking Opinion Mining: The Key to Understanding Public and Customer Sentiment

Discover how opinion mining deciphers customer and public sentiment using AI-driven analysis. Learn how brands can leverage sentiment insigh

[nitor-infotech]

What is Sentiment Analysis? 

Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to determine the sentiment or emotion expressed in a sentence, paragraph, or document. The goal of sentiment analysis is to understand the underlying sentiment behind the text, whether it is positive, negative, or neutral. 

Importance of Sentiment Analysis- 

It enables businesses to gain valuable insights into customer opinions, preferences, and satisfaction levels. 

It allows organizations to monitor their brand reputation in real-time. 

It provides valuable insights into market trends, customer preferences, and competitor performance. 

It is employed in financial markets to analyze news sentiment, social media sentiment, and analyst reports. 

Get further insights on Sentiment Analysis in our blog.  

[droidtown]

文本情感分析的神兵利器 KeyMoji (Emotions in Formula)

KeyMoji 的設計原理

為了徹底解決情緒偵測的問題，KeyMoji 重新定義了「文本情感分析」這個任務！

中文裡有許多能表示「結果貌」的句型。像前述的「 V 得要 X」裡的那個 X 就是 "V" 這個動作的結果或是程度。這種「結果貌」的結構，不只是出現在句型上，在詞彙結構裡也有。像是「摔破」、「殺死」、「印出」…等等詞彙裡，「破」、「死」和「出」都是前面那個動詞執行之後的結果。

相較之下，英文裡的 "to break", "to kill" 和 "to print" 這三個動詞裡，你是看不到究竟有沒有結果產生的。所以在英文裡 "We must kill him many times." 是語意合情合理的句子，因為 "kill" 不一定會「死」。但如果把這句直譯成「我們必需殺死他很多次。」就會有點怪怪的了。因為通常一個生物只能「死一次」。這個問題甚至還有專書裡的章節連結在討論呢！章節名稱就是：

Why "Kill" Does Not Mean "Cause to Die": The Semantics of Action Sentences

如此看來，直接套用適用西方語系的那套 sentiment analysis 的演算法是不適合的。因此，我們依中文的句法結構，定義了幾種句型：

正向表述句型 (e.g., X 終於 Y 了！)

負向表述句型 (e.g., X 是有 Y 嗎？)

趨中性句型 (e.g., X 只是要 Y )

反轉極性句型 (e.g., X 不是有 Y )

這樣就夠了嗎？不，這還不夠！

在語言裡還有一個叫 "Scope" 的概念 (見 參考連結01、參考連結02 或 參考連結03)，它是用來計算某些語意的控制範圍的。比如說「每個」和「某個」都是屬於帶有 "Scope" 的運算子 (operator)。那麼，以這個句子為例：

遊戲裡的每個玩家都選擇某個角色

當「每個」的 scope 大於「某個」的時候，每個玩家扮演的是不同的角色

當「某個」的 scope 大於「每個」的時候，每個玩家扮演的是同一個角色

簡單地說，這些含有 "socping" 語意功能的詞彙，會影響句子語意的計算結果。而我們已經知道像是「每個」、「都」、「不」、「沒有」…這些詞彙都是帶有 "scoping" 語意功能的詞彙。因此在 KeyMoji 裡，我們也加入了計算 Operator 的 "Scope" 的處理。舉例來說：

搞定了 "syntax" 上的句型，算完了 "semantics" 的語意後，我們產生了一段可以計算語意的框架，這時再帶上透過文本計算出來的向量模型中，每個詞彙自帶的正負值進去計算，就得出 KeyMoji 的計算值了。

比較特別的是，對於情緒的定義，我們做了比較嚴格的操作型定義，而不是單純地用人工標記結果做為分類依據：

正/負向情緒指的是發話方企圖表示的情緒，而非受話方感受到的情緒。

若語句中含有「期待」或「意料之外」的表述，則正/負向情緒指的是「符合期待(正)」或「超出期待(負)」的表示語句。

若語句結構為「有一位乖巧的孩子」這種存現句描述句型則為中性，不因「乖巧的」一詞的正/負向而影響。

排除道德與社會善良風俗判斷，所以像是「世界終於被政治正確毀滅了」和「阿哲終於考上台大醫學院了」都是正向情緒。即便第一句的發話方可能有違反社會善良風俗的心理狀態。

在這些前提下，重新設定文本材料的分類目標進行訓練，產生一個模型。 如此一來，就是「句型帶有的資訊」加上「邏輯語意的計算」以及「詞彙帶有的資訊」綜合起來的情緒正負面計算結果囉！

ps. 正因為 KeyMoji 的輸出結果涉及了許多語言學典範轉移以後，我們對人類認知系統的許多理解的計算公式。因此 KeyMoji 真正是名符其實的 "Emotions in formula" 哦！

[後記] 開發背景故事

文本情感分析 (Text Sentiment Analysis) 是自然語言處理中相當抽象的一項任務。這項任務的目標，是找出文本在人類情緒光譜上的分佈位置，是屬於「正向 (正面表述)」或是「負向 (負面表述)」。

光就這個目標的說明，就帶了許多定義模糊的地方，讓 NLP 在進行工程實作時遇到許多挑戰。例如「正向/負向」是依誰的體驗決定呀？是「說這句話的人」還是「聽到這句話的人」的體驗呢？其次，如果有些涉及文化、社會風俗或是道德判斷的詞彙，是不是都要視為是「正向」呢？但不同的文化族群會有不一樣的價值觀呀！

此外，許多基於機器學習的方法裡，透過「人工標注」的結果，好讓電腦可以學習出一個模型來模仿人類的判斷。

但就前面的討論裡就看得出來，兩個人的判斷可能就不是一致的。甚至同一個人在前一個星期和後一個星期的不同時間裡，就可能會做出不一定的判斷結果，並加註在標記裡了。

這些問題在機器學習領域裡「大力出奇蹟」的信念下，雖然有時候會有點效果，但更多的問題用一張圖來說明，就像這樣：

簡單地說，傳統的「情緒偵測」方法，遇到了以下幾個問題：

正負向的定義不明確

正負向的計算基礎可能只有詞彙的人工標記結果

正負向的計算基礎可能只有篇章的人工標記結果

只有正負向，並不能適恰地呈現人類語言的情緒複雜度

如果這些問題不解決的話，尤其在中文這種詞彙語意變化靈活的語言裡，就會出現以下的結果：

這類需要考慮結構的句子交給 Google NLP 或是利用人工標記結果訓練出來的 SnowNLP 做情緒偵測的處理後，都會得到錯誤的結果。

[attorneyandlawyer]

OPINION MINING FOR SOCIAL NETWORKING SITE

OPINION MINING FOR SOCIAL NETWORKING SITE

Abstract

This system uses opinion-mining methodology in order to achieve desired functionality. Opinion Mining for Social Networking Site is a web application where a user will post his views related to some subject and other users will view and comment on it. The System takes comments of various users, based on the opinion, system will specify whether…

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[attorneyandlawyer]

DOCUMENT SENTIMENT ANALYSIS USING OPINION MINING

DOCUMENT SENTIMENT ANALYSIS USING OPINION MINING

TABLE OF CONTENT Title page- – – – – – – – – i Approval page – – – – – – – -ii Dedication – – – – – – – – -iii Acknowledgement – – – – – – – -iv Abstract – – – – – – – – – -v Table of content – – – – – – – -vi

CHAPTER ONE INTRODUCTION – – – – – – – -1 1.0 Background of the study – – – – -1 1.1 Statement of the problem – – – – -5 1.2 Purpose of the…

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[ijtsrd]

Sentiment Analysis on Twitter Dataset using R Language

by B. Nagajothi | Dr. R. Jemima Priyadarsini ""Sentiment Analysis on Twitter Dataset using R Language""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd28071.pdf

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28071/sentiment-analysis-on-twitter-dataset-using-r-language/b-nagajothi

international journals in engineering, call for paper science, ugc list of journals

Sentiment Analysis involves determining the evaluative nature of a piece of text. A product review can express a positive, negative, or neutral sentiment or polarity . Automatically identifying sentiment expressed in text has a number of applications, including tracking sentiment towards Movie reviews and Automobile reviews improving customer relation models, detecting happiness and well being, and improving automatic dialogue systems. The evaluative intensity for both positive and negative terms changes in a negated context, and the amount of change varies from term to term. To adequately capture the impact of negation on individual terms, here proposed to empirically estimate the sentiment scores of terms in negated context from movie review and auto mobile review, and built two lexicons, one for terms in negated contexts and one for terms in affirmative non negated contexts. By using these Affirmative Context Lexicons and Negated Context Lexicons were able to significantly improve the performance of the overall sentiment analysis system on both tasks. This thesis have proposed a sentiment analysis system that detects the sentiment of corpus dataset using movie review and Automobile review as well as the sentiment of a term a word or a phrase within a message term level task using R language. 

[rankmeonline-blog]

What is Sentiment analysis and what are its benefits for a brand?

Here is all you need to know about what sentiment analysis is, its benefits and how does it exactly work.

[ijtsrd]

Mining High Utility Dataset

by Monisha D | Arul Kumar" Mining High Utility Dataset" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, 

URL: http://www.ijtsrd.com/papers/ijtsrd11691.pdf  

Direct URL: http://www.ijtsrd.com/engineering/computer-engineering/11691/ mining-high-utility-dataset/monisha-d

international journals in engineering, call for paper engineering, paper publication for engineering

High Utility Dataset mining is a popular tactics in the data mining, which bond to search all datasets having a profit higher than a customer specified minimum profit point. Although, setting appropriate value is a trouble for the customers. If the point is set to be too low, too many HUDs will be catalyzed, which may result in the mining process very ineffectual. And also, if the point is set to be too high, it results with no Products will be found. Setting value is a problem by proposing a new configuration for high utility dataset mining, where k is the desired number of Products to be mined. The new scheme for utility mining with top-k HUDs in databases will provide algorithm consult on their uses and limits. The experimental estimation on datasets shows the activity of the Tagging and Opinion mining Calculations around the effective utility mining algorithms.-¬-¬