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(guān)文本分析中的可能性以及如何在業(yè)務(wù)場(chǎng)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(yè)的招標(biāo)，每天準(zhǔn)備一份報(bào)告。 我怎么辦呢，因?yàn)樽詈笠粋€(gè)還沒(méi)有整理好； 它的提交日期已經(jīng)到了……”

Presales Team member (Legal/Business)

售前團(tuán)隊(duì)成員(法律/業(yè)務(wù))

“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?cè)趹?yīng)用程序商店中托管的應(yīng)用程序獲得了數(shù)百條評(píng)論……與兼職資源無(wú)關(guān)，以了解那里發(fā)生的事情……”。

App Owner

應(yīng)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(guān)于我們?cè)赥witter上發(fā)布的帖子的見(jiàn)解嗎，請(qǐng)確保我每天都收到它們……”

Head, Digital Channels

頭，數(shù)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(lái)我們?cè)诖蠖鄶?shù)保修索賠中都缺少一些重要的信息，我感覺(jué)到預(yù)定義的數(shù)據(jù)點(diǎn)所提供的信息不足以使許多頻繁索賠的原因跌至最低點(diǎn)……”。

Warranty Claims Manager

保修索賠經(jīng)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(lái)這個(gè)IT請(qǐng)求是為了重置密碼，類似于David最后為另一個(gè)請(qǐng)求標(biāo)記的，但是不確定……。”

IT Service Desk Executive

IT服務(wù)臺(tái)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(wèn)題，他們所面對(duì)的，因此改善的時(shí)機(jī)現(xiàn)有客戶的定性信息。 問(wèn)題在于隱藏在文本內(nèi)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(wèn)題變得更加嚴(yán)重，而在某些情況下，其分析的標(biāo)準(zhǔn)化根據(jù)人類的技能水平和經(jīng)驗(yàn)而有所不同。 這些問(wèn)題陳述中的大多數(shù)解決方案都可用，也可以通過(guò)由機(jī)器學(xué)習(xí)(ML)或(和)深度學(xué)習(xí)(DL)支持的人工智能(AI)的實(shí)施來(lái)開(kāi)發(fā)。

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(lǐng)域，可以歸類為三個(gè)主要類別之一，還將嘗試在更高級(jí)別上簡(jiǎn)要介紹可用的不同解決方案。 我們計(jì)劃在后續(xù)職位中針對(duì)每個(gè)類別涵蓋詳細(xì)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(biāo)記為更簡(jiǎn)潔和有意義的單詞或句子。 從標(biāo)記服務(wù)臺(tái)請(qǐng)求到對(duì)制造工廠中消耗的間接零件進(jìn)行分類以優(yōu)化成本，這在不同行業(yè)中有許多應(yīng)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(lái)看，這確實(shí)是最簡(jiǎn)單的文本分析類型。 隨著NLP引擎一起使用的機(jī)器學(xué)習(xí)算法的出現(xiàn)，它已經(jīng)變得越來(lái)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(fù)責(zé)應(yīng)用程序以及社交媒體渠道的團(tuán)隊(duì)或所有者正在努力尋找發(fā)布新產(chǎn)品或應(yīng)用程序并添加新功能后的客戶體驗(yàn)。 無(wú)論是好是壞，還是好的。 信息流幾乎是24 X 7，也可能會(huì)變成多語(yǔ)言。 因此，更加需要一種系統(tǒng)，該系統(tǒng)可以提供對(duì)企業(yè)從多個(gè)社交媒體平臺(tái)從客戶那里收到的反饋的實(shí)時(shí)情緒分析，并且可能包含多種語(yǔ)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(tái)都提供開(kāi)箱即用的情感分析，并且有許多開(kāi)源解決方案可實(shí)現(xiàn)此目的，而無(wú)需與任何平臺(tái)綁定。 所有這些都非常成熟，高效，并且使用ML開(kāi)發(fā)的模型提供了支持。

Summarization or Text Insights:

總結(jié)或文本見(jiàn)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(tái)以及類似的應(yīng)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(shù)據(jù)？ 用戶輸入的信息有什么特殊之處可以幫助企業(yè)做得更好？ 答案在于通過(guò)為每個(gè)客戶或潛在客戶提供個(gè)性化服務(wù)來(lái)服務(wù)客戶，借助他們的評(píng)論，反饋，觀點(diǎn)等對(duì)客戶或客戶進(jìn)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(gè)領(lǐng)域中，業(yè)務(wù)合作伙伴之間有大量信息以結(jié)構(gòu)化方式流出，盡管生成的信息量離最終用戶不遠(yuǎn)，但是從業(yè)務(wù)影響的角度來(lái)看，這是相當(dāng)重要的。 在這里，我們談?wù)摰氖巧虡I(yè)伙伴之間的合法商業(yè)合同文件交換，這在商業(yè)方面具有相當(dāng)重要的價(jià)值。 一般而言，考慮到現(xiàn)實(shí)世界中對(duì)時(shí)間軸的期望和總體資源可用性，考慮到任何失誤都可能導(dǎo)致重大業(yè)務(wù)損失，團(tuán)隊(duì)對(duì)其進(jìn)行處理可能會(huì)帶來(lái)很大壓力。

Warranty Claims Reviews

保修索賠評(píng)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(guò)服務(wù)代理或其他手動(dòng)方式將原始數(shù)據(jù)轉(zhuǎn)換為結(jié)構(gòu)化數(shù)據(jù)之間，會(huì)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(jiàn)解，即是否有任何模式或趨勢(shì)出現(xiàn)，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(xué)習(xí)和NLP算法進(jìn)行文本分析非常方便，可以提供摘要或見(jiàn)解。 使用它們可以從內(nèi)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

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