文本文件加密和解密

The objective is to provide crisp information about possibilities in Text analytics and how it can be leveraged in business scenarios

目的是提供有關文本分析中的可能性以及如何在業務場景中利用它的清晰信息。

“I got to work on this 500 page of tender, prepare a report in a day. How am I going to do it as still the last one is not sorted out; its submission date has already reached …”

“我必須處理這500頁的招標，每天準備一份報告。 我怎么辦呢，因為最后一個還沒有整理好； 它的提交日期已經到了……”

Presales Team member (Legal/Business)

售前團隊成員(法律/業務)

“we are getting hundreds of reviews for our apps hosted in app store… it’s too much to do with part time resource to understand what’s happening there ….”

“我們在應用程序商店中托管的應用程序獲得了數百條評論……與兼職資源無關，以了解那里發生的事情……”。

App Owner

應用所有者

“Can someone give me insights on posts we are getting on twitter, please make sure I get them on a daily basis ….”

“有人能給我關于我們在Twitter上發布的帖子的見解嗎，請確保我每天都收到它們……”

Head, Digital Channels

頭，數字頻道

“Looks like we are missing some vital information in most of the warranty claims, I am getting a feeling that pre-defined data points are not giving enough information to get to the bottom of the reasons for so many frequent claims ….”

“看起來我們在大多數保修索賠中都缺少一些重要的信息，我感覺到預定義的數據點所提供的信息不足以使許多頻繁索賠的原因跌至最低點……”。

Warranty Claims Manager

保修索賠經理

“Looks like this IT request is for password reset and similar to what David marked lastly for another one but am not sure ….”

“看起來這個IT請求是為了重置密碼，類似于David最后為另一個請求標記的，但是不確定……。”

IT Service Desk Executive

IT服務臺主管

All the above information from different channels holds qualitative information about existing customers as well prospects and the problems they are facing and hence an opportunity for improvement. The problems are pointing to not so easily available information hidden deep inside text. In some cases, problem gets augmented due to the size of text to be processed whereas for some its standardization of analysis which varies based on human skillset as well experience. Solutions to most of these problem statements are available or can be developed with implementation of Artificial intelligence (AI), powered by Machine Learning (ML) or (and) Deep Learning (DL).

者A從不同渠道上面的信息持有約以及前景和問題，他們所面對的，因此改善的時機現有客戶的定性信息。 問題在于隱藏在文本內部的信息不那么容易獲得。 在某些情況下，由于要處理的文本的大小，問題變得更加嚴重，而在某些情況下，其分析的標準化根據人類的技能水平和經驗而有所不同。 這些問題陳述中的大多數解決方案都可用，也可以通過由機器學習(ML)或(和)深度學習(DL)支持的人工智能(AI)的實施來開發。

Solutions for above listed examples comes under Text analytics domain and can be clubbed under one of the three major categories, also will try to brief at higher level what different solutions are available. Detailed solution approach we plan to cover for each category in subsequent posts

上面列出的示例的解決方案屬于“文本分析”領域，可以歸類為三個主要類別之一，還將嘗試在更高級別上簡要介紹可用的不同解決方案。 我們計劃在后續職位中針對每個類別涵蓋詳細的解決方案方法

Classification:

分類：

It’s all about labeling a series of words or sentences to a more concise & meaningful one. This has lots of applications in different industries starting from labeling a service desk request to categorize the indirect parts consumed in manufacturing plants for costs optimization.

這就是將一系列單詞或句子標記為更簡潔和有意義的單詞或句子。 從標記服務臺請求到對制造工廠中消耗的間接零件進行分類以優化成本，這在不同行業中有許多應用。

This is indeed the simplest type of text analytics from algorithmic perspective. It has become more mature over a period with the advent of machine learning algorithms used in tandem with NLP engines

從算法的角度來看，這確實是最簡單的文本分析類型。 隨著NLP引擎一起使用的機器學習算法的出現，它已經變得越來越成熟。

Sentiment Analysis:

情緒分析：

As mentioned in previously listed examples, Teams or Owner responsible for Apps as well social media channels are struggling to find out how is the customer experience post launch of new products or apps with the addition of new features; whether it’s getting good or bad or just ok. The information flow is almost 24 X 7 and may also go multilingual. Hence there is a greater need to have a system which can provide real time sentiment analysis of feedback received from customers by business from multiple social media platforms and possibly accommodate multiple languages.

如前面列出的示例中所述，負責應用程序以及社交媒體渠道的團隊或所有者正在努力尋找發布新產品或應用程序并添加新功能后的客戶體驗。 無論是好是壞，還是好的。 信息流幾乎是24 X 7，也可能會變成多語言。 因此，更加需要一種系統，該系統可以提供對企業從多個社交媒體平臺從客戶那里收到的反饋的實時情緒分析，并且可能包含多種語言。

All AI platforms provides out of box sentiment analysis as well there are many open source solutions available to get this done without any binding to any platform. All these are quite matured & efficient and powered by models developed using ML.

所有AI平臺都提供開箱即用的情感分析，并且有許多開源解決方案可實現此目的，而無需與任何平臺綁定。 所有這些都非常成熟，高效，并且使用ML開發的模型提供了支持。

Summarization or Text Insights:

總結或文本見解：

Online Feedbacks / Blogs / Posts

在線反饋/博客/帖子

What these social media platforms as well similar apps do with data captured by user in form of tweets or posts or online feedback? What’s special in information keyed in by users can help business do better? Answer lies in serving customers by offering personalized service to each customer or prospect by developing a better understanding about them with the help of their reviews, feedback, views etc.

這些社交媒體平臺以及類似的應用程序如何處理用戶以推文或帖子或在線反饋形式捕獲的數據？ 用戶輸入的信息有什么特殊之處可以幫助企業做得更好？ 答案在于通過為每個客戶或潛在客戶提供個性化服務來服務客戶，借助他們的評論，反饋，觀點等對客戶或客戶進行更好的了解。

Legal Contract Documents Analysis

合法合同文件分析

There is another area where quite a lot of information in structured manner flows out between business partners, although the volume of information generated is nowhere near to end users but from business impact perspective, it is quite a significant one. Here we are talking about Legal Business contract documents exchange between business partners which have quite significant value in commercial perspective. Considering the real-world timeline expectations and overall resource availability in general, it could be quite stressful for team to process them considering any lapse can result in significant loss to business.

在另一個領域中，業務合作伙伴之間有大量信息以結構化方式流出，盡管生成的信息量離最終用戶不遠，但是從業務影響的角度來看，這是相當重要的。 在這里，我們談論的是商業伙伴之間的合法商業合同文件交換，這在商業方面具有相當重要的價值。 一般而言，考慮到現實世界中對時間軸的期望和總體資源可用性，考慮到任何失誤都可能導致重大業務損失，團隊對其進行處理可能會帶來很大壓力。

Warranty Claims Reviews

保修索賠評論

Similarly, in case of warranty claims, a significant information gets lost between transformation of raw data to structured one by Service Agents or other manual means. Delving more into informal text provided by end user can provide more insights i.e. if there is any pattern or trend coming up, and can help in making more informed decisions

同樣，在保修索賠的情況下，在通過服務代理或其他手動方式將原始數據轉換為結構化數據之間，會丟失大量信息。 深入研究最終用戶提供的非正式文本可以提供更多的見解，即是否有任何模式或趨勢出現，并有助于做出更明智的決策

Text analytics with help of Deep Learning & NLP algorithms are handy in this scenario to provide summary or insights. Using them one can bring out insights of varied degree from the content (text).

在這種情況下，借助深度學習和NLP算法進行文本分析非常方便，可以提供摘要或見解。 使用它們可以從內容(文本)中獲得不同程度的洞察力。

翻譯自: https://medium.com/@vinishg/decipher-text-insights-and-related-business-use-cases-f7928ada85ec

文本文件加密和解密

總結

以上是生活随笔為你收集整理的文本文件加密和解密_解密文本见解和相关业务用例的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。